Deep Learning Methods for Remote Sensing
Material type:
TextLanguage: English Publication details: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022Description: 1 online resource (344 p.)Content type: - text
- computer
- online resource
- 9783036546292
- 9783036546308
- books978-3-0365-4630-8
- Environmental science, engineering and technology
- History of engineering and technology
- Technology: general issues
- aerial images
- AGB
- alternating decision trees
- attention mechanism
- bivariate statistics
- change detection
- changes detection
- channel-separable ResNet
- chimney
- circularly fully convolutional networks
- complex Morlet wavelet
- convolutional networks
- Convolutional Neural Networks
- cross-layer feature fusion
- cultivated land extraction
- deep learning
- deep learning neural network
- deep neural networks
- deep-learning
- disease classification
- DLNN
- DOA estimation
- ensemble learning
- ensemble model
- ensemble models
- erosion
- extreme events
- faster R-CNN
- feature extraction
- fire classification
- fire segmentation
- flash-flood potential index
- full convolutional network
- fully convolutional feature maps
- fully convolutional network
- fusion
- Generative Adversarial Networks
- geohazard
- geoinformatics
- geometry structure
- gully erosion susceptibility
- hazard map
- high resolution
- high resolution remote sensing image
- high spatial resolution images
- image enhancement
- image segmentation
- intelligent prediction
- machine learning
- mask R-CNN
- meteorological parameters
- multi-scale context
- natural hazard
- network
- NSFs
- object-based
- off-grid
- optical sensors
- outdated building map
- particle swarm optimization
- power transmission lines
- prediction
- PSO
- radar modulation signal
- rainfall
- remote sensing
- remote sensing images
- remote sensing sensors
- rural settlements
- scattered vegetation
- space-frequency pseudo-spectrum
- spatial analysis
- spatial model
- super-resolution
- target detection
- temperature field
- thermophysical parameters
- three-dimensional scene
- time-frequency analysis
- typhoon
- U-Net
- UAV
- unmanned aerial vehicle (UAV)
- very high-resolution
- VHR images
- vibration dampers detection
- vision transformers
- wildfire detection
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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.
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