JP7830376B2 - Geological feature change detection device and geological feature change detection method - Google Patents

Description

This invention relates to a geological feature change detection device and a geological feature change detection method for detecting changes in geological features.

Sensing from aircraft and satellites is used to understand the situation over a wide area on the ground, for example, to assess the extent of damage during a disaster. Furthermore, with advancements in wireless communication technology and the miniaturization and increased performance of hardware such as cameras, real-time sensing using drones has become commonplace today.

In the event of a disaster, it is crucial to quickly assess the extent of the damage in order to conduct swift and accurate evacuation guidance and rescue operations. Factors used to assess damage include, for example, information indicating areas of landslides, flood-induced inundation, and information indicating building collapses and road ruptures/sinkholes. By quickly and accurately gathering this information, disaster damage can be minimized.

Conventional methods for recognizing the extent of damage caused by disasters include, for example, using a recognition model that has been trained on image data of predetermined damage conditions to determine whether or not damage occurred in images taken from above after a disaster, and displaying the damaged areas.

Patent Document 1 describes a device for estimating disaster damage to a building from images taken from above and building polygons acquired before a disaster occurred. Building polygons are data representing the two-dimensional shape of a building (location and area of the building) on the ground surface. This device includes a learning model for estimating the damage status of buildings corresponding to building polygons. The device sets the condition of the building (e.g., the state of blue tarp covering) as the target damage status, outputs the estimation results of the learning model as feature images, and estimates the disaster damage occurring to the building by referring to the feature images corresponding to each building polygon.

Japanese Patent Publication No. 2019-95886

Generally, optical images captured from above or at high altitudes by cameras suffer from geometric distortions due to lens aberrations. This can result in discrepancies between the building's position in the image and pre-acquired building polygons. Even with conventional techniques that eliminate lens aberrations through camera calibration, if the captured image is not aligned with an actual map, the building's position in the image will not match the building polygon. This leads to inaccurate and inaccurate representations of building positions in feature images, hindering the accurate and timely assessment of disaster damage.

Therefore, there is a need for technology that can reduce the difference (positional discrepancy) between the position of geographic features (objects on the ground) in images taken from above and their actual positions, thereby enabling accurate detection of changes in geographic features.

The objective of this invention is to provide a feature change detection device and a feature change detection method that can reduce the difference between the location of a feature obtained from an image and the actual location of the feature, thereby providing information regarding changes in the feature.

The feature change detection device according to the present invention comprises: a camera that captures a comparison image, which is an image of a feature taken from above or a high place; a feature mask estimation unit that generates a feature mask representing the location information of the feature from a past image, which is an image of the feature taken from above or a high place at a time prior to the capture of the comparison image, and from the comparison image; feature area information in which the location information of the feature is pre-registered; a feature mask correction unit that calculates the degree of overlap with the feature registered in the feature area information for each of the feature masks generated from the past image and the feature mask generated from the comparison image, and registers the area corresponding to the feature whose degree of overlap is above a threshold as the corrected feature mask in the corrected mask information; and a feature change detection unit that detects the temporal change of the feature by calculating the difference between the corrected mask information obtained using the feature mask of the past image and the corrected mask information obtained using the feature mask of the comparison image.

The present invention provides a method for detecting changes in geographic features, which is performed on a geographic feature change detection device equipped with geographic feature area information in which the location information of the geographic feature is pre-registered. The method includes a geographic feature mask estimation step in which the geographic feature change detection device generates a geographic feature mask representing the location information of the geographic feature from both a past image, which is an image of the geographic feature taken from the air or a high place at a time prior to the time the comparison image was taken, and the comparison image. The method also includes a step in which the geographic feature change detection device generates a geographic feature mask representing the location information of the geographic feature from the geographic feature mask generated from the past image and the comparison image. The system includes a feature mask correction step in which, for each of the created feature masks, the degree of overlap with the feature registered in the feature area information is calculated, and the area corresponding to the feature whose degree of overlap is greater than or equal to a threshold is registered in the corrected mask information as the corrected feature mask; and a feature change detection step in which the feature change detection device detects the temporal change of the feature by calculating the difference between the corrected mask information obtained using the feature mask of the past image and the corrected mask information obtained using the feature mask of the comparison target image.

According to the present invention, a feature change detection device and a feature change detection method can be provided that reduce the difference between the location of a feature obtained from an image and the actual location of the feature, thereby providing information regarding changes in the feature.

A functional configuration diagram of a geological change detection device according to Embodiment 1 of the present invention. A diagram showing an example of the hardware configuration of a geological feature change detection device. This diagram illustrates the process performed by the geological feature change detection device in Example 1. A diagram illustrating an example of the processing steps in the feature mask correction section. An example flowchart showing the processes performed by a geological feature change detection device. This diagram shows an example of a display screen output by a geological feature change detection device. A functional configuration diagram of a geological change detection device according to Embodiment 2 of the present invention. This diagram illustrates the process performed by the geological feature change detection device in Example 2.

The feature change detection device and method according to the present invention detect changes in a feature that occurred between the time points in which the images were taken, using at least two images of the feature taken from the air or a high place at different times, and previously acquired location information of the feature. According to the present invention, the difference between the location of the feature obtained from the images and the actual location of the feature (previously acquired location) can be reduced, providing information about changes in the feature and enabling accurate detection of changes in the feature.

Conventional technologies require the acquisition and training of a large number of images of buildings affected by disasters that cause changes in geographic features, in order to create a learning model for recognizing geographic features. However, disasters generally occur infrequently, making it extremely difficult to acquire a large number of images suitable for training. This invention eliminates the need to acquire a large number of images necessary for training in order to detect changes in geographic features.

Furthermore, conventional techniques make it difficult to determine whether or not a geographical feature (such as a building) has disappeared. This is because information simply obtained from an image cannot distinguish whether the feature never existed in the first place or has disappeared. For example, it's impossible to determine whether there was an error in the building polygon information and the building never existed in the first place, or whether the building disappeared due to a disaster. This invention makes it easy to determine whether or not a building has disappeared.

The following describes a geological feature change detection device and method according to an embodiment of the present invention, using the drawings. The geological feature change detection method according to the embodiment of the present invention is performed by the geological feature change detection device according to the embodiment of the present invention.

In this specification, "land features" refers to objects existing on the ground, and can include artificial structures such as buildings, roads, and power lines, as well as natural structures such as vegetation. Changes in land features refer to changes in their shape or location, or their disappearance or appearance. Furthermore, location information for land features refers to information indicating the location and area (outline) of a land feature, and is also simply called the location of a land feature.

In the drawings used in this specification, identical or corresponding components are denoted by the same reference numeral, and repeated descriptions of these components may be omitted.

This section describes a geological feature change detection device and method according to Embodiment 1 of the present invention.

Figure 1 is a functional configuration diagram of the feature change detection device 10 according to this embodiment. The feature change detection device 10 according to this embodiment includes a camera 11, and also comprises a feature mask estimation unit 21, a feature mask correction unit 22, and a feature change detection unit 23. Furthermore, the feature change detection device 10 includes past images 31, a feature mask estimation model 32, and feature area information 33.

Camera 11 is an imaging device that captures images of the ground from above or from a high vantage point. Camera 11 may be fixed in any position, or it may be mounted on a controlled mobile device such as an aircraft or drone. Hereinafter, the image captured by camera 11 will be referred to as the comparison image 35. The comparison image 35 includes features captured by camera 11.

The past image 31 is an image of the terrain captured from above or from a high vantage point at a time prior to when the camera 11 captured the comparison image 35. The past location information of the terrain can be determined from the past image 31. The past image 31 is stored in the terrain change detection device 10.

The feature mask estimation model 32 is a model for generating a feature mask 36 from the comparison image 35 and the past image 31. The feature mask 36 is location information of features obtained from the image, that is, information representing the location and area of features obtained from the image. The feature mask 36 includes masks for one or more features. The feature mask estimation model 32 is, for example, a machine learning model that recognizes the location and area of features from an image, and can be pre-created by machine learning using images provided for training. The feature mask estimation model 32 is stored in the feature change detection device 10.

The feature mask estimation unit 21 estimates the feature mask 36 using the feature mask estimation model 32. Specifically, the feature mask estimation unit 21 receives the comparison image 35 captured by the camera 11 and past images 31 that overlap with the comparison image 35 in at least part, and uses the feature mask estimation model 32 to generate a feature mask 36 for both the comparison image 35 and the past images 31. The feature mask estimation unit 21 outputs the generated feature mask 36 to the feature mask correction unit 22.

The feature mask 36 can be represented using location information of the feature (information indicating the location and area of the feature), or information from which location information of the feature can be obtained through any processing. The feature mask 36 can be represented in any format; for example, it may be represented using the vertices of a polygon forming the outline of the feature, or a polygon representing the feature.

The feature area information 33 is pre-registered information regarding the location of a feature (information indicating the location and area of the feature) and is stored in the feature change detection device 10. The feature area information 33 contains location information for predetermined features. The feature area information 33 is information obtained from publicly available information such as maps, and may also be information created by the operator or administrator of the feature change detection device 10. For example, the feature area information 33 may be bitmap data that distinguishes between the area where the feature exists and other areas, or it may be polygon information representing the outline of the feature, such as the base map information published by the Geospatial Information Authority of Japan.

Furthermore, the feature area information 33 may also register information about features formed by the division of a single feature into multiple features, or information about features formed by the integration of multiple features into a single feature. Thus, the unit of features registered in the feature area information 33 (the scope treated as a single feature) can be arbitrarily defined.

The feature mask correction unit 22 calculates the degree of overlap between the feature mask 36a generated from the past image 31 and the feature mask 36b generated from the comparison image 35, and the features registered in the feature area information 33. If the features registered in the feature area information 33 and the feature masks 36a and 36b satisfy predetermined criteria using the degree of overlap, the unit determines that the feature exists. The feature mask correction unit 22 then creates and outputs the corrected mask information 37.

The corrected mask information 37 is information obtained by correcting the feature masks 36 generated from the past image 31 and the comparison image 35 using feature area information 33. The corrected mask information 37 registers the area corresponding to the feature whose existence has been confirmed by the feature area information 33 among the features represented by the feature mask 36, as the corrected feature mask. The corrected mask information 37 also records whether or not each feature registered in the feature area information 33 exists. The corrected mask information 37 can be expressed in any data format, such as text data or image data.

The feature mask correction unit 22 performs the above processing to create corrected mask information 37a using the feature mask 36a generated from the past image 31, and then creates corrected mask information 37b using the feature mask 36b generated from the comparison image 35.

The feature mask correction unit 22 will be explained in detail.

First, the feature mask correction unit 22 obtains the feature mask 36 from the feature mask estimation unit 21. Next, the feature mask correction unit 22 extracts from the feature area information 33 the area where the features included in the obtained feature mask 36 exist (i.e., the area corresponding to the imaging area of the past image 31 and the comparison image 35). Hereafter, the extracted area of the feature area information 33 will be referred to as the reference area of the feature area information 33.

In this embodiment, as an example, we will explain the case where the entire imaging area of the past image 31 and the comparison image 35 match each other. If only a portion of the imaging areas of the past image 31 and the comparison image 35 match, the feature area information 33 corresponding to the matching area may be extracted. Even in this case, the same effects as in this embodiment can be obtained.

Next, the feature mask correction unit 22 aligns the acquired feature mask 36 with the reference area of the feature area information 33. The feature mask correction unit 22 performs this alignment using any method. For example, the imaging area of the camera 11 may be calculated from external parameters representing the camera's installation position and orientation, and the feature area information 33 corresponding to this imaging area may be determined, thereby aligning the feature mask 36b with the reference area of the feature area information 33. Alternatively, the position information of the camera 11 or the mobile object on which the camera 11 is installed (e.g., an aircraft or drone), recorded when the comparison image 35 was captured, may be obtained from GPS information, and this position information may be used to align the feature mask 36b with the reference area of the feature area information 33.

Next, the feature mask correction unit 22 selects one feature from among the features included in the reference area of the feature area information 33. Then, the feature mask correction unit 22 determines whether the selected feature exists based on the degree of overlap between the selected feature and the corresponding mask in the feature mask 36. The feature mask correction unit 22 repeats the above process for other features included in the reference area of the feature area information 33 to create the corrected mask information 37.

The feature mask correction unit 22 can use, for example, the overlapping area between the area of the selected feature (the feature included in the feature area information 33) and the mask corresponding to the selected feature in the feature mask 36 as the degree of overlap used to determine whether or not the selected feature exists. Alternatively, the feature mask correction unit 22 may use the ratio of the above-mentioned overlapping area to the area of the selected feature's region as the degree of overlap. These areas may be represented, for example, by the number of pixels constituting the area on the image.

The feature mask correction unit 22 may redefine a feature formed by dividing a single feature registered in the feature area information 33 into multiple features as a new feature, or it may redefine a feature formed by integrating multiple features into a single feature as a new feature. For example, if the feature is a building, one building may be divided into four parts, and each of these four features may be registered as a new feature. In this way, if only a part of a single building is destroyed by a disaster or other event, the loss of a portion of the building can be detected.

Furthermore, the feature mask correction unit 22 may specify a particular area of the feature and change the method for determining the presence or absence of the feature in this area compared to other areas. For example, if the feature is a reservoir dam, the vertices of the dam's outline can often be recognized, but the recognized area of other parts changes dynamically, for example, depending on the water level of the reservoir. In such cases, the area other than the vertices of the dam's outline can be designated as a specific area, and in this specific area, the threshold for the degree of overlap used for determination can be set lower than that of the dam's vertices, or dynamically changed according to the water level of the reservoir dam received from the input unit 12 shown in Figure 2 (described later).

The feature change detection unit 23 detects the temporal changes in features between the time the past image 31 was captured and the time the comparison image 35 was captured. The feature change detection unit 23 calculates the difference between the corrected mask information 37a obtained using the feature mask 36a of the past image 31 and the corrected mask information 37b obtained using the feature mask 36b of the comparison image 35. The difference calculated by the feature change detection unit 23 (the difference between the corrected mask information 37a and the corrected mask information 37b) represents the features that have changed from the time the past image 31 was captured to the time the comparison image 35 was captured.

The feature change detection unit 23 extracts the difference between at least two corrected mask information sets 37, which were created from images captured at different points in time. This difference represents the temporal change of the feature detected by the feature change detection unit 23. The feature change detection unit 23 outputs the obtained difference as feature change information 34.

The feature change information 34 is information about the temporal changes of features detected by the feature change detection unit 23. The feature change information 34 may include information recording whether or not there have been changes in the features included in the comparison image 35.

Figure 2 shows an example of the hardware configuration of the feature change detection device 10 according to this embodiment. In addition to the camera 11 shown in Figure 1, the feature change detection device 10 includes an input unit 12, an output unit 13, a CPU (Central Processing Unit) 14, a main memory 15, and an auxiliary memory 16.

The input unit 12 can be configured, for example, with a keyboard or mouse, and receives input from the operator of the terrain change detection device 10. The input unit 12 also receives images and information required by the terrain change detection device 10, such as the comparison image 35 captured by the camera 11.

The output unit 13 has a screen, such as a liquid crystal display, and is used to display various information to the operator. The output unit 13 may also include a storage device, such as a hard disk drive, and may, for example, save the output of the geological change detection device 10 in the form of image data.

The CPU 14 controls the feature change detection device 10 and performs data calculations. The CPU 14 loads various programs and data into the main memory 15 and executes these programs to realize the functions of the feature change detection device 10 (for example, the feature mask estimation unit 21, the feature mask correction unit 22, and the feature change detection unit 23).

The main memory 15 can be configured, for example, as RAM (Random Access Memory). The main memory 15 stores programs that implement functions such as the feature mask estimation unit 21, the feature mask correction unit 22, and the feature change detection unit 23.

The auxiliary storage device 16 can be configured as, for example, a hard disk drive. The auxiliary storage device 16 stores various types of data, such as past images 31, feature mask estimation models 32, and feature area information 33.

Figure 3 illustrates the processing performed by the feature change detection device 10 in Embodiment 1. Figure 3 shows the past image 31, the comparison image 35, the feature mask 36a generated from the past image 31, the feature mask 36b generated from the comparison image 35, the corrected mask information 37a obtained by correcting the feature mask 36a, and the corrected mask information 37b obtained by correcting the feature mask 36b.

Figure 3 shows an example where the feature is a building, and the feature change detection device 10 detects changes in the imaged building. The feature mask 36 includes the building mask. The corrected mask information 37 includes the corrected building mask.

Past image 31 includes buildings T1, T2, T3, T4, and T5 as features.

Comparison image 35 was taken after past image 31 was taken. Assume a landslide occurred between the time past image 31 was taken and the time comparison image 35 was taken. Comparison image 35 shows buildings T1, T2, T4, and T5 after they have been swept away and disappeared due to the mudslide. In comparison image 35, building T3 is still present.

The feature mask estimation unit 21 receives the past image 31 and the comparison image 35 as input, generates a feature mask 36a for the past image 31, and generates a feature mask 36b for the comparison image 35.

In the example shown in Figure 3, the feature mask estimation unit 21 uses semantic segmentation, a type of machine learning algorithm, as the feature mask estimation model 32. This algorithm determines whether each pixel in the image is included in the recognition target. For example, the feature mask estimation unit 21 estimates a feature mask 36 in the image, where areas containing buildings are represented by 1s and other areas by 0s. Figure 3 shows this feature mask 36 displayed as feature mask 36a and feature mask 36b.

Generally, estimation results from machine learning algorithms, including semantic segmentation, contain errors. For example, in the example shown in Figure 3, building T4 is actually a long rectangle in the north-south direction (vertical direction in Figure 3), as shown in the historical image 31. However, in the feature mask 36a, the shape of the mask corresponding to building T4 is elliptical, and a mask that accurately follows the original contour of building T4 is not obtained. Such errors are common in machine learning algorithms.

Therefore, if the change in a feature is determined by calculating the difference between feature mask 36a and feature mask 36b, as in conventional techniques, the above-mentioned error (the error between the actual feature and the mask representing this feature in feature mask 36) may be detected as the difference. Thus, conventional techniques have the problem of confusing the above-mentioned error with the building change that should be detected.

In this embodiment, to solve this problem, the feature mask correction unit 22 corrects the feature mask 36 using the feature area included in the feature area information 33. Specifically, the feature mask correction unit 22 determines the degree of overlap between the feature mask 36a and the features registered in the feature area information 33, and determines that the feature exists if the feature and the feature mask 36a satisfy predetermined determination conditions using the degree of overlap. Then, the feature mask correction unit 22 determines the degree of overlap between the feature mask 36b and the features registered in the feature area information 33, and determines that the feature exists if the feature and the feature mask 36b satisfy predetermined determination conditions using the degree of overlap.

The feature mask correction unit 22 performs this determination and corrects the feature masks 36a and 36b by determining the feature masks 36a and 36b corresponding to the existing feature. It then obtains corrected mask information 37a obtained by correcting feature mask 36a and corrected mask information 37b obtained by correcting feature mask 36b.

The feature mask correction unit 22 performs the above processing on each feature registered in the feature area information 33, thereby creating and outputting corrected mask information 37a and corrected mask information 37b.

The feature change detection unit 23 extracts and calculates the difference between the corrected mask information 37a and the corrected mask information 37b, and outputs the calculated difference as feature change information 34.

The following describes the processing details of the feature mask correction unit 22.

Figure 4 illustrates an example of the processing content of the feature mask correction unit 22. As an example, Figure 4 shows a case where the detected feature is a building, and the feature area information 33 is polygon information representing the outline of the feature.

Figure 4 shows feature mask 36c as a representative feature mask 36 of feature masks 36a and 36b. Feature mask 36c includes four masks M as features (buildings): mask M1, mask M2, mask M3, and mask M4.

Furthermore, Figure 4 shows feature area information 33c as the reference area of feature area information 33 (the area where features included in feature mask 36 exist). Feature area information 33c contains four feature areas R, namely feature area R1, feature area R2, feature area R3, and feature area R4.

Furthermore, Figure 4 shows an overlay diagram 41, which is a diagram in which the feature mask 36c and the feature area information 33c are superimposed.

Furthermore, Figure 4 shows a diagram illustrating the corrected mask information 37c obtained from the feature mask 36c and feature area information 33c using the superimposed diagram 41.

In the example shown in Figure 4, the feature mask correction unit 22 receives the feature mask 36c as input, calculates the degree of overlap between the mask M included in the feature mask 36c and the feature (feature area R) registered in the feature area information 33c, and creates corrected mask information 37c. It is assumed that the feature area R is represented by a polygon representing the outline of a building. Masks M1 to M4 correspond to feature areas R1 to R4, respectively.

Furthermore, in the example shown in Figure 4, the feature mask correction unit 22 uses the overlap ratio as the overlap degree, which is the criterion for determining whether or not each building exists. The overlap ratio is the ratio of the overlapping area to the area of the feature region R. The overlapping area is the area where the feature region R and the area of the feature mask 36c corresponding to the feature region R (mask M) overlap. A threshold of 50% is used for the overlap ratio. That is, the feature mask correction unit 22 determines that a building exists if the overlap ratio is 50% or higher. Note that this threshold is not limited to 50% and can be set to any value.

In the following explanation, the condition used by the feature mask correction unit 22 to determine whether or not a feature exists, based on the degree of overlap, is referred to as the predetermined determination condition. In the example above, the predetermined determination condition is that a building is determined to exist if the degree of overlap is above a threshold (overlap rate of 50% or more).

Furthermore, the criteria for determining whether or not a building (landmark) exists are not limited to the overlap ratio; any arbitrary criteria can be used. For example, the absolute value of the overlapping area may be used. As mentioned above, any value can be used for the threshold of the determination criterion. These determination criteria and thresholds may be pre-stored in the auxiliary storage device 16, or they may be set by the operator of the landmark change detection device 10 via the input unit 12.

In the example shown in Figure 4, when the feature mask 36c and feature area information 33c are superimposed, as shown in the superimposed figure 41, the ratio of the overlapping area between feature area R1 and mask M1 (overlap ratio) to the area of feature area R1 is 50% or more. Since the overlap ratio is above the threshold (50%), mask M1 successfully recognizes the building corresponding to feature area R1.

The feature mask correction unit 22 determines that a building exists in feature area R1 because the overlap ratio is greater than or equal to a threshold. Then, in the corrected mask information 37c, the feature mask correction unit 22 designates the area corresponding to feature area R1 as the area where a building exists. In other words, the feature mask correction unit 22 records the feature area R1 indicated by mask M1 as an existing building in the corrected mask information 37c.

The feature mask correction unit 22 determines that a building exists in feature area R2 because the overlap ratio of mask M2 with respect to feature area R2 is above a threshold (overlap diagram 41). Therefore, in the corrected mask information 37c, the area corresponding to feature area R2 is treated as the area where a building exists. In other words, the feature mask correction unit 22 records the feature area R2 indicated by mask M2 as an existing building in the corrected mask information 37c.

Furthermore, feature area R4 does not overlap with any of the masks M1 to M4 included in feature mask 36c, and the overlapping area and overlapping ratio are zero (overlay diagram 41). The feature mask correction unit 22 determines that since the overlapping ratio for feature area R4 is below the threshold, there is no building corresponding to feature area R4.

Furthermore, in the example shown in Figure 4, as shown in the superimposed diagram 41, the mask M3 does not overlap with any of the feature areas R registered in the feature area information 33c. Therefore, it can be inferred that the mask M3 appeared because the feature mask estimation unit 21 misidentified a building.

Furthermore, as shown in the superimposed diagram 41, although mask M4 overlaps with feature area R3, the overlap rate is less than the threshold (50%). Therefore, it can be inferred that mask M4 appeared because the feature mask estimation unit 21 misidentified a building. The feature mask correction unit 22 then determines that no building exists corresponding to feature area R3.

The feature mask correction unit 22 performs the above processing for each of the feature regions R1 to R4 to create corrected mask information 37c. In the corrected mask information 37c, the feature mask 36c is corrected using the feature region information 33c, and corrected building masks M11 and M22 corresponding to masks M1 and M2 are shown. Furthermore, in the corrected mask information 37c, the contour shapes of the corrected building masks M11 and M22 are corrected by the feature region information 33c, and are modified from masks M1 and M2 to match feature regions R1 and R2, respectively.

Figure 4 shows an example where the corrected mask information 37c is information in the format of a bitmap image outputting the corrected feature area information. The corrected mask information 37 may also be, for example, polygon information representing the feature's outline, and can be expressed in any data format.

As mentioned above, even with general machine learning algorithms that utilize models trained on ample data, minute errors can occur in the generation of the feature mask 36. Therefore, in conventional techniques, when detecting changes in features by calculating the difference between the feature mask 36a and feature mask 36b generated for each of the past image 31 and the comparison image 35, not only the difference due to the actual change in the feature but also the difference due to minute errors in the feature mask 36 appear as noise. Consequently, in conventional techniques, false alarms and missed alarms can occur due to this noise.

In this embodiment, corrected mask information 37, which is information obtained by correcting the feature mask 36 using feature area information 33, is used to compare corrected mask information 37a obtained from past images 31 with corrected mask information 37b obtained from comparison images 35. In the corrected mask information 37a and 37b, the location information of the features is obtained based on the location information of the features pre-registered in the feature area information 33 (i.e., the feature mask 36 is corrected). Therefore, in this embodiment, changes in features that occur over time can be detected without being affected by errors that occurred during the generation of the feature mask 36.

Figure 5 is an example flowchart showing the process performed by the geological feature change detection device 10 according to this embodiment.

In step S101, the feature mask estimation unit 21 receives the past image 31 and at least one comparison image 35 as input, and generates a feature mask 36 for each of the past image 31 and comparison image 35. For the sake of clarity, in the following explanation, we will consider the case where there is only one comparison image 35, and refer to the feature mask 36 corresponding to the past image 31 as feature mask 36a, and the feature mask 36 corresponding to the comparison image 35 as feature mask 36b.

In step S102, the feature mask correction unit 22 receives the feature mask 36 (36a, 36b) as input and extracts the region from the feature region information 33 in which the features included in the acquired feature mask 36 exist (i.e., the region corresponding to the imaging region of the past image 31 and the comparison image 35). This extraction can be performed, for example, using the positional information (coordinate values) contained in the past image 31 and the comparison image 35.

In step S103, the feature mask correction unit 22 selects one feature mask 36 that has not been corrected in the correction process described later. If multiple feature masks 36 are input, the feature mask correction unit 22 performs the correction process on all of them. This embodiment describes the case where the feature mask correction unit 22 first selects feature mask 36a, and then selects feature mask 36b.

Steps S104 to S108 are correction processes for the feature mask 36.

In step S104, the feature mask correction unit 22 selects one region from the feature region information 33 extracted in step S102 (reference region of feature region information 33) where a determination of whether or not a feature exists (feature existence determination) has not been performed.

In step S105, the feature mask correction unit 22 calculates the degree of overlap between the feature region selected in step S104 and the feature mask 36. As previously explained, the degree of overlap is a value used to determine whether or not the selected feature exists (a value used under predetermined determination conditions). In this embodiment, the overlap ratio, as previously explained, is used as the degree of overlap. The overlap ratio is the ratio of the overlapping area to the area of the feature region selected in step S104, and the overlapping area is the area where this feature region overlaps with the area of the feature mask 36 corresponding to this feature region.

In step S106, the feature mask correction unit 22 determines, according to predetermined criteria, whether the overlap rate calculated in step S105 is equal to or greater than a preset threshold. If the overlap rate is equal to or greater than the threshold, the feature mask correction unit 22 determines that a feature exists corresponding to the feature area selected in step S104, and performs the process in step S107. On the other hand, if the overlap rate is less than the threshold, the feature mask correction unit 22 determines that no feature exists corresponding to the feature area selected in step S104, and performs the process in step S108.

In step S107, the feature mask correction unit 22 determines that a feature exists corresponding to the feature area selected in step S104. Therefore, in the corrected mask information 37, it designates the area corresponding to this feature area as the area where the feature exists. That is, the feature mask correction unit 22 records the feature area indicated by the feature mask 36 as an existing feature in the corrected mask information 37.

At least one corrected mask information 37 is created for each feature mask 36. In this embodiment, the feature mask correction unit 22 creates two corrected mask information 37a, corresponding to feature mask 36a, and corrected mask information 37b, corresponding to feature mask 36b.

In step S108, the feature mask correction unit 22 determines whether or not a feature exists (feature existence determination) has been performed for all feature regions registered in the feature region information 33 extracted in step S102. If there are feature regions for which the feature existence determination has not been performed, the feature mask correction unit 22 performs the process in step S104 and selects one new feature region for which the feature existence determination has not been performed. If the feature existence determination has been performed for all feature regions, the feature mask correction unit 22 performs the process in step S109.

In step S109, the feature mask correction unit 22 determines whether the correction process described above (steps S104 to S108) has been performed on all feature masks 36 generated in step S101. If the correction process has been performed on all feature masks 36, the feature change detection unit 23 performs the process in step S110. If the correction process has not been performed on all feature masks 36, the feature mask correction unit 22 performs the process in step S103. In this embodiment, the feature mask correction unit 22 first selects feature mask 36a, and then selects feature mask 36b. If the correction process has not been performed on feature mask 36b, the feature mask correction unit 22 performs the process in step S103 and selects feature mask 36b.

In step S110, the feature change detection unit 23 compares the multiple corrected mask information 37 created in step S107 and records the difference. Specifically, the feature change detection unit 23 calculates the difference between the corrected mask information 37a obtained by correcting the feature mask 36a of the past image 31 and the corrected mask information 37b obtained by correcting the feature mask 36b of the comparison image 35, and outputs the calculated difference as feature change information 34. In this way, the feature change detection unit 23 detects changes in features between the time the past image 31 was captured and the time the comparison image 35 was captured.

For example, if the objective is to detect features that have disappeared between the time the past image 31 was captured and the time the comparison image 35 was captured, the feature change detection unit 23 calculates the result of subtracting the corrected mask information 37b from the corrected mask information 37a, and records the obtained result as feature change information 34.

Furthermore, for example, if the feature is a building, and the objective is to detect a newly constructed building between the time the past image 31 was captured and the time the comparison image 35 was captured, the result of subtracting the corrected mask information 37a from the corrected mask information 37b is obtained, and the resulting result is recorded as feature change information 34.

In this embodiment, we have described an example where one feature mask 36 contains only masks representing one type of feature (for example, the feature mask 36c in Figure 4 contains only masks M1 to M4 representing buildings). However, one feature mask 36 can contain masks representing multiple types of features (e.g., buildings and roads).

Furthermore, a feature mask 36 can be generated for each type of feature. For example, if the features are buildings and roads, two types of feature masks 36 may be generated: one containing a mask representing buildings and another containing a mask representing roads. In this case, in step S102, the region containing the features represented by the feature masks 36 is extracted from the feature region information 33.

Figure 6 shows an example of the display screen 60 output by the geological feature change detection device 10 according to this embodiment. The output unit 13 (Figure 2) displays the display screen 60 on a screen such as a liquid crystal display.

The display screen 60 shown in Figure 6 comprises a display area 61 for past images 31, a display area 65 for comparison images 35, and a display area 63 for an image (detection result image) showing the changes in features detected by the feature change detection unit 23. The display area 61 for past images 31 displays the past image 31 for the area corresponding to the comparison image 35. The display area 65 for comparison images 35 displays, for example, the comparison image 35 captured by the camera 11.

The display area 63 of the detection result image displays, for example, an image obtained from the difference between the corrected mask information 37a for the past image 31 and the corrected mask information 37b for the comparison image 35. That is, the detection result image displays features that have changed between the corrected mask information 37a obtained using the feature mask 36a of the past image 31 and the corrected mask information 37b obtained using the feature mask 36b of the comparison image 35.

In the display area 63 of the detection result image in Figure 6, as an example, a building that has collapsed and disappeared is displayed. That is, a building that exists in the past image 31 but not in the comparison image 35. This building is a feature that has changed between the corrected mask information 37a for the past image 31 and the corrected mask information 37b for the comparison image 35.

Furthermore, the display screen 60 may include a setting area 67. The setting area 67 is an area for the operator of the feature change detection device 10 to set the image to be displayed on the display screen 60.

The operator can change the information displayed in the display area 63 of the detection result image by manipulating the setting area 67 using the input unit 12 (Figure 2). For example, the operator can select the features (buildings) to display in the display area 63 using checkboxes or radio buttons in the setting area 67. In the example shown in Figure 6, the operator can display collapsed buildings or existing buildings. Furthermore, for example, the operator can change the overlap threshold used by the feature mask correction unit 22 when determining the presence of features using a slider in the setting area 67.

Furthermore, the display screen 60 may display the feature area information 33 and the feature mask 36 generated by the feature mask estimation unit 21 overlaid on the past image 31 and the comparison image 35, by the operator operating the setting area 67.

Furthermore, the display screen 60 may, for example, display information about a feature in an additional window such as a pop-up when the operator moves the mouse cursor over a feature in the past image 31 using the mouse on the input unit 12. Examples of such information include the feature ID, which is a serial number for managing the feature, and the result of the feature's existence determination (e.g., whether the building has disappeared or still exists).

The feature change detection device 10 according to this embodiment can include location information of objects not registered as features in the feature area information 33. That is, the feature area information 33 may also contain location information of objects not registered as features. Hereinafter, objects that are not registered as features but whose location information is known will be referred to as non-feature objects. The feature area information 33 can contain location information of non-feature objects as map information, etc.

Non-terrain objects include objects that do not appear in past images 31 or comparison images 35, such as buried water pipes, fiber optic cables, and power lines. Above-ground power lines can also be included as non-terrain objects if they do not appear in past images 31 or comparison images 35.

The feature change detection unit 23 uses feature area information 33 to refer to the position information of non-feature objects and can estimate changes in non-feature objects based on the results of detecting changes in features at the same location as the non-feature object or in its vicinity. For example, the feature change detection unit 23 estimates that non-feature objects at the same location as or in the vicinity of the feature whose change was detected have changed in the same way as the feature between the time the past image 31 was captured and the time the comparison image 35 was captured.

For example, suppose the geographical feature is a road, and the non-geographical feature is a water pipe buried underground beneath this road. The geographical feature change detection unit 23 detects an event such as a fracture or subsidence in the road as a geographical feature change. In this case, if the water pipe is located at the same position as the road or in the vicinity of the road, the geographical feature change detection unit 23 estimates that the water pipe is also damaged, similar to the road. Specifically, when the geographical feature change detection unit 23 detects a road fracture, if the water pipe is located at the same position as the fracture or within a certain radius of the fracture, it considers the water pipe to be highly likely to be damaged, and therefore records the water pipe as damaged or as an object requiring verification.

Through the above processing, the feature change detection device 10 according to this embodiment can estimate changes in objects that are not registered as features or objects that do not appear in the image.

As described above, the feature change detection device 10 according to this embodiment reduces the difference between the location of the feature obtained from the image and the actual location of the feature by calculating the degree of overlap with the features registered in the feature area information 33 for each of the feature masks 36a generated from the past image 31 and the feature mask 36b generated from the comparison image 35, thereby creating corrected mask information 37 and providing information about changes in the feature.

This section describes a geological feature change detection device and method according to Embodiment 2 of the present invention. The following primarily explains the differences between this embodiment and Embodiment 1.

In Example 1, the feature change detection device 10 generates a feature mask 36 from images captured at different time points (past image 31 and comparison image 35), and detects changes in features from the difference between the corrected mask information 37 (37a, 37b) created using the feature mask 36.

In this embodiment, by estimating and generating a ground attribute mask in addition to the feature mask 36 for both the comparison image 35 and the past image 31, it is possible to provide information about changes in features even when the changes in features are ambiguous.

Ground attributes refer to objects that exist between a feature and the camera 11, obstructing the image of the feature captured by the camera 11. In other words, in the image captured by the camera 11, the feature overlaps with and is hidden by the ground attributes, resulting in parts or all of it being obscured.

A feature/ground attribute mask is a general term encompassing both feature masks and ground attribute masks. A ground attribute mask represents the location information of ground attributes obtained from an image; that is, information representing the location and area of ground attributes obtained from the image. Therefore, a feature/ground attribute mask represents the location information of features and ground attributes obtained from an image; that is, information representing the location and area of features and ground attributes obtained from the image. Since a feature/ground attribute mask includes both feature masks and ground attribute masks, it contains masks for one or more features and ground attributes.

Figure 7 is a functional configuration diagram of the feature change detection device 10 according to this embodiment. The feature change detection device 10 according to this embodiment differs from Embodiment 1 in that the configuration of the feature mask estimation unit 21 and the feature mask correction unit 22 are different, and it also differs from Embodiment 1 in that it includes a feature/ground attribute mask estimation model 32a instead of the feature mask estimation model 32.

The feature/ground attribute mask estimation model 32a is a model for generating feature/ground attribute masks 38 (i.e., both feature masks and ground attribute masks) from the comparison image 35 and the past image 31. The feature/ground attribute mask estimation model 32a can be pre-created, for example, using machine learning, and is stored in the feature change detection device 10.

The feature mask estimation unit 21 uses the feature/ground attribute mask estimation model 32a to estimate and generate feature/ground attribute masks 38 for both the comparison image 35 and the past image 31. The feature mask estimation unit 21 outputs the generated feature/ground attribute masks 38 to the feature mask correction unit 22.

The feature mask correction unit 22 calculates the degree of overlap with features registered in the feature area information 33 for each of the feature/ground attribute masks 38a generated from the past image 31 and the feature/ground attribute mask 38b generated from the comparison image 35, and creates and outputs corrected mask information 37. In this embodiment, the corrected mask information 37 is information obtained by correcting the feature/ground attribute masks 38 generated from the past image 31 and the comparison image 35 using the feature area information 33. The corrected mask information 37 includes information on features represented by the feature/ground attribute mask 38 whose existence has been confirmed by the feature area information 33, with the area corresponding to these features registered as a corrected feature mask, as well as information on features that require verification based on the feature/ground attribute mask 38 and the feature area information 33 (features requiring verification).

The feature mask correction unit 22 determines that a feature exists if it satisfies the predetermined determination conditions (the predetermined determination conditions using the degree of overlap, as explained in Example 1) with respect to the feature mask. Furthermore, the feature mask correction unit 22 determines that a feature is a feature with added ground attributes if it satisfies the predetermined determination conditions with respect to the ground attribute mask. A feature with added ground attributes is a feature that, in the image captured by the camera 11, overlaps with the ground attributes, is obscured by the shadow of the ground attributes, and is partially or completely not visible. In this embodiment, using the predetermined determination conditions in Example 1, the feature mask correction unit 22 calculates the degree of overlap between the feature/ground attribute mask 38 (feature mask and ground attribute mask) and the features registered in the feature area information 33.

The feature mask correction unit 22, by performing the above processing, creates corrected mask information 37a using the feature/ground attribute mask 38a generated from the past image 31, and creates corrected mask information 37b using the feature/ground attribute mask 38b generated from the comparison image 35. Furthermore, if there are features with added ground attributes, the feature mask correction unit 22 outputs information about those features to the feature change detection unit 23.

The feature change detection unit 23 extracts and calculates the difference between the corrected mask information 37a and the corrected mask information 37b, and outputs the calculated difference as feature change information 34. Furthermore, if a feature with added ground attributes exists, the feature with added ground attributes is designated as a feature requiring verification, and information about this feature is output. Features with added ground attributes are features that overlap with ground attributes in the image, and their actual existence may be unclear due to the ground attributes. Therefore, features with added ground attributes are designated as features requiring verification to determine their actual existence.

The output unit 13 (Figure 2) can display, for example, a surface with added ground attributes as a surface requiring verification on a screen such as a liquid crystal display.

Figure 8 illustrates the processing performed by the feature change detection device 10 in Example 2. Figure 8 shows the past image 31, the comparison image 35, the feature/ground attribute mask 38a generated from the past image 31, the feature/ground attribute mask 38b generated from the comparison image 35, the corrected mask information 37a obtained by correcting the feature/ground attribute mask 38a, and the corrected mask information 37b obtained by correcting the feature/ground attribute mask 38b.

Figure 8 shows an example where the feature is a building, the ground attribute is vegetation, and the feature change detection device 10 detects changes in the imaged building. Note that the ground attribute is not limited to vegetation; for example, it could be clouds, power lines, or flying objects. The feature/ground attribute mask 38 includes the building mask (which is the feature mask) and the vegetation mask (which is the ground attribute mask). The corrected mask information 37 includes the corrected building mask (which is the corrected building mask) and the ground attribute-added feature 39.

Past image 31 includes buildings T1, T2, T3, T4, and T5 as features, and also includes vegetation P as a ground attribute. In past image 31, buildings T3 and T4 are overlapped with and hidden by vegetation P, and parts of them are not visible.

Comparison image 35 was taken after past image 31 was taken. Assume a landslide occurred between the time past image 31 was taken and the time comparison image 35 was taken. Comparison image 35 shows buildings T1, T2, T4, and T5 after they have been swept away and disappeared due to the mudslide. In comparison image 35, building T3 is still present.

The feature mask estimation unit 21 receives the past image 31 and the comparison image 35 as input. It generates a feature/ground attribute mask 38a for the past image 31 and a feature/ground attribute mask 38b for the comparison image 35. The feature/ground attribute mask 38a includes feature masks (building masks) corresponding to buildings T1 to T5 and ground attribute masks (vegetation masks) corresponding to vegetation P. The feature/ground attribute mask 38b includes only the vegetation mask.

In the example shown in Figure 8, the feature mask estimation unit 21 uses semantic segmentation, a type of machine learning algorithm, as the feature/ground attribute mask estimation model 32a, determining whether each pixel in the image is included in the recognition target. For example, the feature mask estimation unit 21 estimates a feature/ground attribute mask 38 in the image, where areas containing buildings are represented by 1, areas containing vegetation by 2, and other areas by 0. Figure 8 shows this feature/ground attribute mask 38 displayed as an image, labeled as feature/ground attribute mask 38a and feature/ground attribute mask 38b.

The feature mask estimation unit 21 may generate multiple types of feature/ground attribute masks 38. For example, the feature mask estimation unit 21 may generate two types of feature/ground attribute masks 38: one where areas containing buildings are represented by 1 and other areas by 0, and another where areas containing vegetation are represented by 1 and other areas by 0. The feature mask estimation unit 21 can thus generate feature/ground attribute masks 38 represented in any way.

The feature mask correction unit 22 calculates the degree of overlap between the feature/ground attribute mask 38a and the features registered in the feature area information 33. If the feature and its mask satisfy predetermined criteria, the unit determines that the feature exists. If the feature and its ground attribute mask satisfy predetermined criteria, the unit determines that the feature is a ground attribute-added feature 39. The feature mask correction unit 22 then calculates the degree of overlap between the feature/ground attribute mask 38b and the features registered in the feature area information 33. If the feature and its mask satisfy predetermined criteria, the unit determines that the feature exists. If the feature and its ground attribute mask satisfy predetermined criteria, the unit determines that the feature is a ground attribute-added feature 39. Note that the criteria between a feature and its mask, and the criteria between a feature and its ground attribute mask, may differ, for example, in terms of the definition of overlap and the threshold for overlap.

The feature mask correction unit 22 performs the above processing on each of the features included in the feature/ground attribute mask 38a and the feature/ground attribute mask 38b, thereby creating and outputting corrected mask information 37a obtained by correcting the feature/ground attribute mask 38a, and corrected mask information 37b obtained by correcting the feature/ground attribute mask 38b.

In the example shown in Figure 8, the presence of building T3, which is a ground attribute-added feature 39, in the corrected mask information 37a and 37b is because building T3 is registered in the feature area information 33 and is a feature that overlaps with the ground attribute mask (vegetation mask) of the feature/ground attribute masks 38a and 38b. Building T3, which is a ground attribute-added feature 39, is a feature that requires verification.

The feature change detection unit 23 extracts and calculates the difference between the corrected mask information 37a and the corrected mask information 37b, and outputs the calculated difference as feature change information 34.

In the example shown in Figure 8, both the historical image 31 and the comparison image 35 include vegetation P in the foreground. Therefore, in both the historical image 31 and the comparison image 35, a portion of the building T3 is not captured due to the ground-level vegetation P, making it difficult to accurately grasp the changes in the building T3.

In this embodiment of the feature change detection device 10, features that cannot be detected because their entirety is not imaged due to the influence of ground attributes are designated as features with added ground attributes 39. This allows the operator of the feature change detection device 10 to be informed that the feature cannot be detected due to ground attributes. For example, in the example shown in Figure 8, the feature change detection device 10 may record building T3, which is a feature with added ground attributes 39, as a feature requiring verification and display it on the display screen 60. Building T3 is a feature requiring verification; it was not detected by the feature/ground attribute mask 38b due to ground attributes (vegetation P), but it may actually exist. Therefore, upon seeing the display screen 60 indicating that building T3 is a feature requiring verification, the operator can, for example, treat building T3 similarly to a lost building and use this information to formulate operations such as dispatching a rescue team.

Although the comparison image is not shown, the following example is also conceivable. For instance, suppose vegetation P and building T3 existed when the past image 31 (Figure 8) was captured, but did not exist when the comparison image was captured. Furthermore, suppose the comparison image does not capture vegetation P or building T3, but instead captures the ground at the location of building T3.

In this case, the feature corresponding to building T3 is registered as a ground attribute-added feature 39 in the corrected mask information 37a, and as a non-existent feature in the corrected mask information 37b. At the time the past image 31 was captured, building T3 was registered as a ground attribute-added feature 39 and is a feature requiring verification, so its existence is uncertain. However, since it is confirmed that building T3 does not exist at the time the comparison image was captured, it is registered as a lost feature. Note that the conditions under which the feature change detection unit 23 extracts the difference are not limited to the above embodiment, but include various examples.

Furthermore, the present invention is not limited to the embodiments described above, and various modifications are possible. For example, the embodiments described above are detailed explanations provided to clearly illustrate the present invention, and the present invention is not necessarily limited to embodiments comprising all the described configurations. Also, it is possible to replace parts of the configuration of one embodiment with those of another embodiment. It is also possible to add configurations from other embodiments to the configuration of one embodiment. Furthermore, it is possible to delete parts of the configuration of each embodiment, or to add or replace other configurations.

10...Feature change detection device, 11...Camera, 12...Input unit, 13...Output unit, 14...CPU, 15...Main memory, 16...Auxiliary memory, 21...Feature mask estimation unit, 22...Feature mask correction unit, 23...Feature change detection unit, 31...Past image, 32...Feature mask estimation model, 32a...Feature/ground attribute mask estimation model, 33, 33c...Feature area information, 34...Feature change information, 35...Comparison target image, 36, 36a, 36b, 36c...Ground Object mask, 37, 37a, 37b, 37c... Corrected mask information, 38, 38a, 38b... Feature/ground attribute mask, 39... Feature with added ground attribute, 41... Overlay map, 60... Display screen, 61... Display area of past images, 63... Display area of detection result image, 65... Display area of comparison target image, 67... Setting area, M, M1-M4... Mask, M11, M22... Corrected building mask, P... Vegetation, R, R1-R4... Feature area, T1-T5... Building.

Claims (9)

A camera that captures comparison images, which are images of geographical features taken from above or at a high altitude,
A feature mask estimation unit generates a feature mask representing the location information of the feature from a past image, which is an image of the feature taken from above or at a high altitude at a time prior to the acquisition of the comparison image, and from the comparison image.
The location information of the aforementioned feature is registered in advance as feature area information,
A feature mask correction unit calculates the degree of overlap with the feature registered in the feature area information for each of the feature masks generated from the aforementioned past image and the feature mask generated from the aforementioned comparison image, and registers the area corresponding to the feature whose degree of overlap with the feature is greater than or equal to a threshold as the corrected feature mask in the corrected mask information.
A feature change detection unit detects the temporal change of a feature by calculating the difference between the corrected mask information obtained using the feature mask of the past image and the corrected mask information obtained using the feature mask of the comparison target image,
A geological feature change detection device characterized by comprising the following: Equipped with a feature mask estimation model created using machine learning,
The feature mask estimation unit generates the feature mask using the feature mask estimation model.
The geological feature change detection device according to claim 1. Objects existing between the camera and the feature are defined as ground attributes.
The feature mask estimation unit generates a feature/ground attribute mask representing the positional information of the feature and the ground attribute from the past image and the comparison image, respectively.
The geological feature change detection device according to claim 1. The feature/ground attribute mask comprises the feature mask and a ground attribute mask representing the location information of the ground attribute.
The aforementioned feature mask correction unit is
For each of the feature/ground attribute masks generated from the aforementioned past images and the feature/ground attribute masks generated from the aforementioned comparison images, the degree of overlap with the features registered in the feature area information is calculated.
The region corresponding to the feature whose degree of overlap with the aforementioned ground attribute mask is greater than or equal to a threshold is registered in the corrected mask information as a feature with added ground attributes.
The geological feature change detection device according to claim 3. It has an output unit with a screen,
The output unit displays on the screen the features that have changed between the corrected mask information obtained using the feature mask of the past image and the corrected mask information obtained using the feature mask of the comparison image.
The geological feature change detection device according to claim 1. It has an output unit with a screen,
The output unit displays the ground attribute-added feature on the screen.
The geological feature change detection device according to claim 4. The aforementioned feature area information includes location information of objects that are not registered as features.
The feature change detection unit estimates that an object located at the same or nearby position as the feature whose time change was detected has changed between the time the past image was captured and the time the comparison image was captured.
The geological feature change detection device according to claim 1. Images of geographical features taken from above or from a high vantage point are used as comparison images.
The location information of the aforementioned feature is executed by a feature change detection device that has pre-registered feature area information,
The feature change detection device performs a feature mask estimation step, which generates a feature mask representing the location information of the feature from a past image, which is an image of the feature taken from above or at a high altitude at a time prior to the time the comparison target image was taken, and from the comparison target image.
The feature change detection device performs a feature mask correction step in which it calculates the degree of overlap with the feature registered in the feature area information for each of the feature masks generated from the past image and the feature mask generated from the comparison target image, and registers the area corresponding to the feature whose degree of overlap with the feature is greater than or equal to a threshold as the corrected feature mask in the corrected mask information.
The feature change detection device performs a feature change detection step in which it detects the temporal change of the feature by calculating the difference between the corrected mask information obtained using the feature mask of the past image and the corrected mask information obtained using the feature mask of the comparison target image,
A method for detecting changes in geological features, characterized by comprising the following: Objects existing between the camera that captured the comparison image and the feature are defined as ground attributes.
In the feature mask estimation step, a feature/ground attribute mask representing the positional information of the feature and ground attributes is generated from the past image and the comparison image, respectively.
The feature/ground attribute mask comprises the feature mask and a ground attribute mask representing the location information of the ground attribute.
In the feature mask correction step,
For each of the feature/ground attribute masks generated from the aforementioned past images and the feature/ground attribute masks generated from the aforementioned comparison images, the degree of overlap with the features registered in the feature area information is calculated.
The region corresponding to the feature whose degree of overlap with the aforementioned ground attribute mask is greater than or equal to a threshold is registered in the corrected mask information as a feature with added ground attributes.
The method for detecting changes in geological features according to claim 8.