Reprint
Remote Sensing for Target Object Detection and Identification
Edited by
March 2020
336 pages
- ISBN978-3-03928-332-3 (Paperback)
- ISBN978-3-03928-333-0 (PDF)
This book is a reprint of the Special Issue Remote Sensing for Target Object Detection and Identification that was published in
Engineering
Environmental & Earth Sciences
Summary
Target object detection and identification are among the primary uses for a remote sensing system. This is crucial in several fields, including environmental and urban monitoring, hazard and disaster management, and defense and military. In recent years, these analyses have used the tremendous amount of data acquired by sensors mounted on satellite, airborne, and unmanned aerial vehicle (UAV) platforms. This book promotes papers exploiting different remote sensing data for target object detection and identification, such as synthetic aperture radar (SAR) imaging and multispectral and hyperspectral imaging. Several cutting-edge contributions, which provide examples of how to select of a technology or another depending on the specific application, will be detailed.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
anomaly detection; hyperspectral imagery; low-rank representation; dictionary construction; HSI reconstruction; sparse coding; adaptive weighting; infrared small target detection; local prior analysis; nonconvex tensor robust principle component analysis; partial sum of the tensor nuclear norm; low rank sparse decomposition; Lp-norm constraint; non-convex optimization; alternating direction method of multipliers; infrared small target detection; convolutional neural networks (CNNs); object detection; remote sensing images; contextual information; part-based; multi-model; very-high-resolution (VHR) remote sensing imagery; object detection; multi-scale pyramidal features; multi-scale strategies; oil tank detection; unsupervised saliency model; Color Markov Chain; bottom-up and top-down; hazard prevention; flood hazard; hidden danger identification; tower failure; vehicle detection; object matching; superpixel segmentation; unmanned aerial vehicle; remote sensing imagery; thermal infrared target tracking; semantic features; mask sparse representation; particle filter framework; ADMM; satellite videos; region proposals; convolutional neural networks; tiny and dim target detection; component mixture model; object detection; remote sensing image; deep learning; convolutional neural networks (CNNs); hardware architecture; processor; ground-based detection; infrared imaging; observability; detecting distance; earth entry vehicle; synthetic aperture radar (SAR); rivers water-flow elevation estimation; pixel-tracking; phase unwrapping; infrared small-faint target detection; non-independent and identical distribution (non-i.i.d.) mixture of Gaussians; flux density; variational Bayesian; target detection; target identification; SAR; visible; infrared; hyperspectral