Artificial Intelligence-Based Learning Approaches for Remote Sensing

Edited by
December 2022
382 pages
  • ISBN978-3-0365-6083-0 (Hardback)
  • ISBN978-3-0365-6084-7 (PDF)

This book is a reprint of the Special Issue Artificial Intelligence-Based Learning Approaches for Remote Sensing that was published in

Environmental & Earth Sciences

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.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
pine wilt disease dataset; GIS application visualization; test-time augmentation; object detection; hard negative mining; video synthetic aperture radar (SAR); moving target; shadow detection; deep learning; false alarms; missed detections; synthetic aperture radar (SAR); on-board; ship detection; YOLOv5; lightweight detector; remote sensing image; spectral domain translation; generative adversarial network; paired translation; synthetic aperture radar; ship instance segmentation; global context modeling; boundary-aware box prediction; land-use and land-cover; built-up expansion; probability modelling; landscape fragmentation; machine learning; support vector machine; frequency ratio; fuzzy logic; artificial intelligence; remote sensing; interferometric phase filtering; sparse regularization (SR); deep learning (DL); neural convolutional network (CNN); semantic segmentation; open data; deep learning; building extraction; unet; deeplab; classifying-inversion method; AIS; atmospheric duct; artificial intelligence; synthetic aperture radar (SAR); ship detection and classification; rotated bounding box; deep learning (DL); attention; feature alignment; weather nowcasting; deep learning; ResNeXt; radar data; deep learning; spectral-spatial interaction network; spectral-spatial attention; pansharpening; UAV visual navigation; Siamese network; multi-order feature; MIoU; imbalanced data classification; data over-sampling; generative adversarial network; graph convolutional network; semi-supervised learning; remote sensing; atmospheric duct; troposcatter; tropospheric turbulence; intercity co-channel interference; deep learning; concrete bridge; visual inspection; defect; deep convolutional neural network; transfer learning; interpretation techniques; weakly supervised semantic segmentation; n/a