Deep Learning Methods for Remote Sensing
Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest...
Sábháilte in:
| Formáid: | Online |
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| Teanga: | Béarla |
| Foilsithe / Cruthaithe: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Ábhair: | |
| Rochtain ar líne: | ONIX_20221117_9783036546308_107 |
| Clibeanna: |
Níl clibeanna ann, Bí ar an gcéad duine le clib a chur leis an taifead seo!
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| _version_ | 1869527126441459712 |
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| collection | Directory of Open Access Books |
| description | Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing. |
| format | Online |
| id | doab-20.500.12854ir-93850 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-938502024-04-11T15:11:06Z Deep Learning Methods for Remote Sensing Akhloufi, Moulay A. Shahbazi, Mozhdeh full convolutional network U-Net cultivated land extraction deep learning remote sensing target detection high resolution remote sensing image chimney faster R-CNN spatial analysis super-resolution Generative Adversarial Networks Convolutional Neural Networks disease classification changes detection fully convolutional feature maps outdated building map VHR images gully erosion susceptibility deep learning neural network DLNN particle swarm optimization PSO geohazard geoinformatics ensemble model erosion hazard map spatial model natural hazard extreme events rural settlements fully convolutional network multi-scale context high spatial resolution images flash-flood potential index remote sensing sensors bivariate statistics alternating decision trees ensemble models deep-learning fusion mask R-CNN object-based optical sensors scattered vegetation very high-resolution off-grid DOA estimation circularly fully convolutional networks space-frequency pseudo-spectrum high resolution typhoon rainfall convolutional networks image segmentation prediction ensemble learning machine learning feature extraction AGB NSFs radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet remote sensing images change detection attention mechanism cross-layer feature fusion power transmission lines vibration dampers detection unmanned aerial vehicle (UAV) deep neural networks wildfire detection fire classification fire segmentation vision transformers UAV aerial images three-dimensional scene temperature field intelligent prediction network geometry structure meteorological parameters thermophysical parameters thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing. 2022-11-17T16:27:45Z 2022-11-17T16:27:45Z 2022 book ONIX_20221117_9783036546308_107 9783036546308 9783036546292 https://directory.doabooks.org/handle/20.500.12854/93850 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/6279 https://mdpi.com/books/pdfview/book/6279 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-4630-8 10.3390/books978-3-0365-4630-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036546308 9783036546292 344 Basel open access |
| spellingShingle | full convolutional network U-Net cultivated land extraction deep learning remote sensing target detection high resolution remote sensing image chimney faster R-CNN spatial analysis super-resolution Generative Adversarial Networks Convolutional Neural Networks disease classification changes detection fully convolutional feature maps outdated building map VHR images gully erosion susceptibility deep learning neural network DLNN particle swarm optimization PSO geohazard geoinformatics ensemble model erosion hazard map spatial model natural hazard extreme events rural settlements fully convolutional network multi-scale context high spatial resolution images flash-flood potential index remote sensing sensors bivariate statistics alternating decision trees ensemble models deep-learning fusion mask R-CNN object-based optical sensors scattered vegetation very high-resolution off-grid DOA estimation circularly fully convolutional networks space-frequency pseudo-spectrum high resolution typhoon rainfall convolutional networks image segmentation prediction ensemble learning machine learning feature extraction AGB NSFs radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet remote sensing images change detection attention mechanism cross-layer feature fusion power transmission lines vibration dampers detection unmanned aerial vehicle (UAV) deep neural networks wildfire detection fire classification fire segmentation vision transformers UAV aerial images three-dimensional scene temperature field intelligent prediction network geometry structure meteorological parameters thermophysical parameters thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology Deep Learning Methods for Remote Sensing |
| title | Deep Learning Methods for Remote Sensing |
| title_full | Deep Learning Methods for Remote Sensing |
| title_fullStr | Deep Learning Methods for Remote Sensing |
| title_full_unstemmed | Deep Learning Methods for Remote Sensing |
| title_short | Deep Learning Methods for Remote Sensing |
| title_sort | deep learning methods for remote sensing |
| topic | full convolutional network U-Net cultivated land extraction deep learning remote sensing target detection high resolution remote sensing image chimney faster R-CNN spatial analysis super-resolution Generative Adversarial Networks Convolutional Neural Networks disease classification changes detection fully convolutional feature maps outdated building map VHR images gully erosion susceptibility deep learning neural network DLNN particle swarm optimization PSO geohazard geoinformatics ensemble model erosion hazard map spatial model natural hazard extreme events rural settlements fully convolutional network multi-scale context high spatial resolution images flash-flood potential index remote sensing sensors bivariate statistics alternating decision trees ensemble models deep-learning fusion mask R-CNN object-based optical sensors scattered vegetation very high-resolution off-grid DOA estimation circularly fully convolutional networks space-frequency pseudo-spectrum high resolution typhoon rainfall convolutional networks image segmentation prediction ensemble learning machine learning feature extraction AGB NSFs radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet remote sensing images change detection attention mechanism cross-layer feature fusion power transmission lines vibration dampers detection unmanned aerial vehicle (UAV) deep neural networks wildfire detection fire classification fire segmentation vision transformers UAV aerial images three-dimensional scene temperature field intelligent prediction network geometry structure meteorological parameters thermophysical parameters thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| topic_facet | full convolutional network U-Net cultivated land extraction deep learning remote sensing target detection high resolution remote sensing image chimney faster R-CNN spatial analysis super-resolution Generative Adversarial Networks Convolutional Neural Networks disease classification changes detection fully convolutional feature maps outdated building map VHR images gully erosion susceptibility deep learning neural network DLNN particle swarm optimization PSO geohazard geoinformatics ensemble model erosion hazard map spatial model natural hazard extreme events rural settlements fully convolutional network multi-scale context high spatial resolution images flash-flood potential index remote sensing sensors bivariate statistics alternating decision trees ensemble models deep-learning fusion mask R-CNN object-based optical sensors scattered vegetation very high-resolution off-grid DOA estimation circularly fully convolutional networks space-frequency pseudo-spectrum high resolution typhoon rainfall convolutional networks image segmentation prediction ensemble learning machine learning feature extraction AGB NSFs radar modulation signal time–frequency analysis complex Morlet wavelet image enhancement channel-separable ResNet remote sensing images change detection attention mechanism cross-layer feature fusion power transmission lines vibration dampers detection unmanned aerial vehicle (UAV) deep neural networks wildfire detection fire classification fire segmentation vision transformers UAV aerial images three-dimensional scene temperature field intelligent prediction network geometry structure meteorological parameters thermophysical parameters thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TQ Environmental science, engineering and technology |
| url | ONIX_20221117_9783036546308_107 |