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...

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Bibliografiske detaljer
Format:	Online
Sprog:	engelsk
Udgivet:	MDPI - Multidisciplinary Digital Publishing Institute 2022
Fag:	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Networks Convolutional Neural Networks disease classification changes detection fully convolutional feature maps outdated building map VHR images gully erosion susceptibility deep learning neural network DLNN particle swarm optimization PSO geohazard geoinformatics ensemble model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
Online adgang:	ONIX_20221117_9783036546308_107
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ONIX_20221117_9783036546308_107

Deep Learning Methods for Remote Sensing

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