Reggiannini et al., 2021 - Google Patents
Mesoscale Patterns Identification through SST Image Processing.Reggiannini et al., 2021
View PDF- Document ID
- 8191248777797597967
- Author
- Reggiannini M
- Janeiro J
- Martins F
- Papini O
- Pieri G
- Publication year
- Publication venue
- ROBOVIS
External Links
Snippet
Mesoscale marine phenomena represent important features to understand and include within predictive models, which provide valuable information for proper environmental policy making. For example the rearrangement of the organic substances, consequent to the …
- 238000004458 analytical method 0 abstract description 10
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
- G06K9/00657—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas of vegetation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30861—Retrieval from the Internet, e.g. browsers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wan et al. | A small-patched convolutional neural network for mangrove mapping at species level using high-resolution remote-sensing image | |
| Bartalev et al. | Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique | |
| Sharma et al. | A Machine Learning and Cross‐Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data | |
| Bèland et al. | Assessment of land‐cover changes related to shrimp aquaculture using remote sensing data: a case study in the Giao Thuy District, Vietnam | |
| Zhang et al. | Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors | |
| Samiappan et al. | Mapping of invasive phragmites (common reed) in Gulf of Mexico coastal wetlands using multispectral imagery and small unmanned aerial systems | |
| Reggiannini et al. | Mesoscale Patterns Identification through SST Image Processing. | |
| Pirotta et al. | Multi-scale analysis reveals changing distribution patterns and the influence of social structure on the habitat use of an endangered marine predator, the sperm whale Physeter macrocephalus in the Western Mediterranean Sea | |
| Zhao et al. | Isolating individual trees in a closed coniferous forest using small footprint lidar data | |
| Brum-Bastos et al. | Multi-source data fusion of optical satellite imagery to characterize habitat selection from wildlife tracking data | |
| Chen et al. | The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data | |
| Xiao et al. | Optimal and robust vegetation mapping in complex environments using multiple satellite imagery: Application to mangroves in Southeast Asia | |
| Wang et al. | The role of remote sensing in species distribution models: a review | |
| Yu et al. | Multi-temporal remote sensing of land cover change and urban sprawl in the coastal city of Yantai, China | |
| Garcia et al. | Relict landslide detection using deep-learning architectures for image segmentation in rainforest areas: a new framework | |
| Lee et al. | Pre-trained feature aggregated deep learning-based monitoring of overshooting tops using multi-spectral channels of GeoKompsat-2A advanced meteorological imagery | |
| Avelar et al. | Analysis of land use and land cover change in a coastal area of Rio de Janeiro using high-resolution remotely sensed data | |
| Sudiana et al. | Monitoring the distribution of mangrove area using synthetic aperture radar (sar) and optic remote sensing data fusion based on deep learning in kotabaru regency, indonesia | |
| Tsutsumida et al. | Mapping cherry blossom phenology using a semi-automatic observation system with street level photos | |
| Giuliani | Time-first approach for land cover mapping using big Earth observation data time-series in a data cube–a case study from the Lake Geneva region (Switzerland) | |
| Lei et al. | PhenoCropNet: A phenology-aware-based SAR crop mapping network for cloudy and rainy areas | |
| Ahmad et al. | Enhanced urban impervious surface land use mapping using a novel multi-sensor feature fusion method and remote sensing data | |
| Ye et al. | Pine wilt disease monitoring using multimodal remote sensing data and feature classification | |
| Miao et al. | A novel attention-based early fusion multi-modal cnn approach to identify soil erosion based on unmanned aerial vehicle | |
| Thwal et al. | Land cover classification and change detection analysis of multispectral satellite images using machine learning |