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Artificial Intelligence-Based Learning Approaches for Remote Sensing
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The reprint focuses on artificial intelligence-based learning approaches and their applications in remote sensing fields. The explosive development of machine learning, deep learning approaches and its wide applications in signal processing have been witnessed in remote sensing. The new developments in remote sensing have led to a high resolution monitoring of ground on a global scale, giving a huge amount of ground observation data. Thus, artificial intelligence-based deep learning approaches and its applied signal processing are required for remote sensing. These approaches can be universal or specific tools of artificial intelligence, including well known neural networks, regression methods, decision trees, etc. It is worth compiling the various cutting-edge techniques and reporting on their promising applications.
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Keywords
- AIS
- artificial intelligence
- atmospheric duct
- Attention
- boundary-aware box prediction
- building extraction
- built-up expansion
- classifying-inversion method
- concrete bridge
- data over-sampling
- deep convolutional neural network
- deep learning
- Deep Learning (DL)
- deeplab
- defect
- Environmental science, engineering & technology
- false alarms
- feature alignment
- frequency ratio
- Fuzzy logic
- generative adversarial network
- GIS application visualization
- global context modeling
- graph convolutional network
- hard negative mining
- History of engineering & technology
- imbalanced data classification
- intercity co-channel interference
- interferometric phase filtering
- interpretation techniques
- land-use and land-cover
- Landscape fragmentation
- lightweight detector
- Machine learning
- MIoU
- missed detections
- moving target
- multi-order feature
- n/a
- neural convolutional network (CNN)
- object detection
- on-board
- open data
- paired translation
- pansharpening
- pine wilt disease dataset
- probability modelling
- radar data
- Remote sensing
- remote sensing image
- ResNeXt
- rotated bounding box
- semantic segmentation
- semi-supervised learning
- shadow detection
- ship detection
- ship detection and classification
- ship instance segmentation
- Siamese network
- sparse regularization (SR)
- spectral domain translation
- spectral-spatial attention
- spectral-spatial interaction network
- support vector machine
- synthetic aperture radar
- Synthetic Aperture Radar (SAR)
- Technology, engineering, agriculture
- Technology: general issues
- test-time augmentation
- transfer learning
- troposcatter
- tropospheric turbulence
- UAV visual navigation
- UNet
- video synthetic aperture radar (SAR)
- visual inspection
- weakly supervised semantic segmentation
- weather nowcasting
- YOLOv5
Links
DOI: 10.3390/books978-3-0365-6084-7Editions