Onojeghuo et al., 2023 - Google Patents
Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical ImageryOnojeghuo et al., 2023
View HTML- Document ID
- 16353358306695884938
- Author
- Onojeghuo A
- Miao Y
- Blackburn G
- Publication year
- Publication venue
- Remote Sensing
External Links
Snippet
Rice is a globally significant staple food crop. Therefore, it is crucial to have adequate tools for monitoring changes in the extent of rice paddy cultivation. Such a system would require a sustainable and operational workflow that employs open-source medium to high spatial and …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/01—Social networking
-
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Talukdar et al. | Land-use land-cover classification by machine learning classifiers for satellite observations—A review | |
Zhang et al. | Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier | |
Du et al. | Smallholder crop area mapped with a semantic segmentation deep learning method | |
Kwak et al. | Impact of texture information on crop classification with machine learning and UAV images | |
Xu et al. | Crop classification based on temporal information using sentinel-1 SAR time-series data | |
Aguilar et al. | A cloud-based multi-temporal ensemble classifier to map smallholder farming systems | |
Li et al. | Object-based crop classification with Landsat-MODIS enhanced time-series data | |
Heenkenda et al. | Mangrove species identification: Comparing WorldView-2 with aerial photographs | |
Peña et al. | Object-based image classification of summer crops with machine learning methods | |
Yin et al. | Optimizing feature selection of individual crop types for improved crop mapping | |
Seydi et al. | A dual attention convolutional neural network for crop classification using time-series Sentinel-2 imagery | |
Atzberger et al. | Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets | |
Nalepa | Recent advances in multi-and hyperspectral image analysis | |
Jia et al. | A hybrid deep learning-based spatiotemporal fusion method for combining satellite images with different resolutions | |
Chen et al. | Multi-feature object-based change detection using self-adaptive weight change vector analysis | |
Kiala et al. | Feature selection on sentinel-2 multispectral imagery for mapping a landscape infested by parthenium weed | |
Hao et al. | The potential of time series merged from Landsat-5 TM and HJ-1 CCD for crop classification: a case study for Bole and Manas Counties in Xinjiang, China | |
Xu et al. | Evaluation of one-class support vector classification for mapping the paddy rice planting area in Jiangsu Province of China from Landsat 8 OLI imagery | |
Fu et al. | Mapping impervious surfaces in town–rural transition belts using China’s GF-2 imagery and object-based deep CNNs | |
Wang et al. | Mapping paddy rice using weakly supervised long short-term memory network with time series sentinel optical and SAR images | |
Onojeghuo et al. | Deep ResU-Net Convolutional Neural Networks Segmentation for Smallholder Paddy Rice Mapping Using Sentinel 1 SAR and Sentinel 2 Optical Imagery | |
Li et al. | A novel efficient method for land cover classification in fragmented agricultural landscapes using sentinel satellite imagery | |
Liang et al. | Mapping vegetation at species level with high-resolution multispectral and lidar data over a large spatial area: A case study with Kudzu | |
Wang et al. | Object-based mapping of gullies using optical images: A case study in the black soil region, Northeast of China | |
Hao et al. | Segmentation scale effect analysis in the object-oriented method of high-spatial-resolution image classification |