TY  - BOOK
TI  - Deep Learning Methods for Remote Sensing
SN  - 9783036546292
PY  - 2022///
CY  - Basel
PB  - MDPI - Multidisciplinary Digital Publishing Institute
KW  - Environmental science, engineering and technology
KW  - bicssc
KW  - History of engineering and technology
KW  - Technology: general issues
KW  - aerial images
KW  - AGB
KW  - alternating decision trees
KW  - attention mechanism
KW  - bivariate statistics
KW  - change detection
KW  - changes detection
KW  - channel-separable ResNet
KW  - chimney
KW  - circularly fully convolutional networks
KW  - complex Morlet wavelet
KW  - convolutional networks
KW  - Convolutional Neural Networks
KW  - cross-layer feature fusion
KW  - cultivated land extraction
KW  - deep learning
KW  - deep learning neural network
KW  - deep neural networks
KW  - deep-learning
KW  - disease classification
KW  - DLNN
KW  - DOA estimation
KW  - ensemble learning
KW  - ensemble model
KW  - ensemble models
KW  - erosion
KW  - extreme events
KW  - faster R-CNN
KW  - feature extraction
KW  - fire classification
KW  - fire segmentation
KW  - flash-flood potential index
KW  - full convolutional network
KW  - fully convolutional feature maps
KW  - fully convolutional network
KW  - fusion
KW  - Generative Adversarial Networks
KW  - geohazard
KW  - geoinformatics
KW  - geometry structure
KW  - gully erosion susceptibility
KW  - hazard map
KW  - high resolution
KW  - high resolution remote sensing image
KW  - high spatial resolution images
KW  - image enhancement
KW  - image segmentation
KW  - intelligent prediction
KW  - machine learning
KW  - mask R-CNN
KW  - meteorological parameters
KW  - multi-scale context
KW  - natural hazard
KW  - network
KW  - NSFs
KW  - object-based
KW  - off-grid
KW  - optical sensors
KW  - outdated building map
KW  - particle swarm optimization
KW  - power transmission lines
KW  - prediction
KW  - PSO
KW  - radar modulation signal
KW  - rainfall
KW  - remote sensing
KW  - remote sensing images
KW  - remote sensing sensors
KW  - rural settlements
KW  - scattered vegetation
KW  - space-frequency pseudo-spectrum
KW  - spatial analysis
KW  - spatial model
KW  - super-resolution
KW  - target detection
KW  - temperature field
KW  - thermophysical parameters
KW  - three-dimensional scene
KW  - time-frequency analysis
KW  - typhoon
KW  - U-Net
KW  - UAV
KW  - unmanned aerial vehicle (UAV)
KW  - very high-resolution
KW  - VHR images
KW  - vibration dampers detection
KW  - vision transformers
KW  - wildfire detection
N1  - Open Access
N2  - 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
UR  - https://directory.doabooks.org/handle/20.500.12854/93850
UR  - https://mdpi.com/books/pdfview/book/6279
ER  -