Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images

Edited by
June 2021
438 pages
  • ISBN978-3-0365-0986-0 (Hardback)
  • ISBN978-3-0365-0987-7 (PDF)

This book is a reprint of the Special Issue Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images that was published in

Environmental & Earth Sciences
The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
  • Hardback
© by the authors
synthetic aperture radar; despeckling; multi-scale; LSTM; sub-pixel; high-resolution remote sensing imagery; multi-scale; road extraction; machine learning; DenseUNet; scene classification; lifting scheme; convolution; CNN; image classification; deep features; hand-crafted features; Sinkhorn loss; remote sensing; text image matching; triplet networks; EfficientNets; LSTM network; convolutional neural network; water identification; water index; semantic segmentation; high-resolution remote sensing image; pixel-wise classification; result correction; conditional random field (CRF); satellite; object detection; neural networks; single-shot; deep learning; convolutional neural network; global convolution network; feature fusion; depthwise atrous convolution; high-resolution representations; ISPRS vaihingen; Landsat-8; object detection; faster region-based convolutional neural network (FRCNN); single-shot multibox detector (SSD); super-resolution; remote sensing imagery; edge enhancement; satellites; scene classification; open-set domain adaptation; adversarial learning; min-max entropy; pareto ranking; synthetic aperture radar; SAR; Sentinel–1; Open Street Map; deep learning; U–Net; desert; road; infrastructure; mapping; monitoring; deep convolutional networks; outline extraction; misalignments; nearest feature selector; hyperspectral image classification; two stream residual network; deep learning; Batch Normalization; plant disease detection; precision agriculture; UAV multispectral images; machine learning; orthophotos registration; 3D information; orthophotos segmentation; wildfire detection; convolutional neural networks; densenet; generative adversarial networks; CycleGAN; data augmentation; pavement markings; deep learning; visibility; framework; urban forests; OUDN algorithm; deep learning; object-based; high spatial resolution remote sensing; deep learning; Generative Adversarial Networks; post-disaster; building damage assessment; anomaly detection; Unmanned Aerial Vehicles (UAV); satellite; xBD; machine learning; generative adversarial networks; feature engineering; orthophoto; unsupervised segmentation