Deep Learning and Computer Vision in Remote Sensing-II

doi:10.3390/books978-3-0365-9365-4

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Deep Learning and Computer Vision in Remote Sensing-II

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November 2023

378 pages

ISBN978-3-0365-9364-7 (Hardback)
ISBN978-3-0365-9365-4 (PDF)

https://doi.org/10.3390/books978-3-0365-9365-4

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This book is a reprint of the Special Issue Deep Learning and Computer Vision in Remote Sensing-II that was published in

Engineering

Environmental & Earth Sciences

Summary

Computer Vision (CV) have seen a massive rise in popularity in the remote sensing field over the last few years. This success is mostly due to the effectiveness of deep learning (DL) algorithms. However, remote sensing data acquisition and annotation, as well as information extraction from massive remote sensing data, are still challenging. This reprint collected novel developments in the field of deep learning and computer vision methods for remote sensing. Papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems, have been published. With practical examples and real-world case studies, this reprint provides a valuable resource for researchers, professionals, and students seeking to harness the power of deep learning in the field of remote sensing. Here are some major topics that are addressed in this reprint: Satellite image processing and analysis based on deep learning; Deep learning for object detection, image classification, and semantic and instance segmentation; Deep learning for remote sensing scene understanding and classification; Transfer learning, deep reinforcement learning for remote sensing; Supervised and unsupervised representation learning for remote sensing environments.

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Keywords

pose estimation; landmark regression; space target; 1D landmark representation; deep learning; convolutional neural network (CNN); deep supervision; lightweight model; remote sensing; semantic segmentation; convolutional neural networks (CNNs); remote sensing images; object detection; knowledge inference module; remote sensing; convolutional neural networks; tree ensemble methods; multi-label classification; semantic segmentation; complex-valued U-Net; complex-valued capsule network; polarimetric synthetic aperture radar; unmanned aerial vehicle (UAV); deep learning; object detection; grassland grazing livestock; remote sensing image; artificial intelligence; deep learning; remote sensing; semantic segmentation; building extraction; remote sensing images; multi-scale object detection; multi-feature fusion and attention network; multi-branch convolution; attention mechanism; loss function; semantic segmentation; remote-sensing image; neural architecture search; sparse regularization; HRNet; Earth observation; remote sensing; deep learning; semantic segmentation; object detection; land use and land cover classification; object detection; transfer learning; dynamic resolution adaptation; small-object detection; attention mechanism; remote sensing; semantic segmentation; machine learning; data augmentation; automatic target recognition; synthetic aperture radar; spacecraft recognition; few-shot feature adaptation; generative family; neural processes; semantic segmentation; deep learning; remote sensing imagery; transformer; Landsat