Advances in Hyperspectral Data Exploitation

doi:10.3390/books978-3-0365-5796-0

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Advances in Hyperspectral Data Exploitation

Edited by

November 2022

434 pages

ISBN978-3-0365-5795-3 (Hardback)
ISBN978-3-0365-5796-0 (PDF)

https://doi.org/10.3390/books978-3-0365-5796-0

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This book is a reprint of the Special Issue Advances in Hyperspectral Data Exploitation that was published in

Engineering

Environmental & Earth Sciences

Summary

Using hyperspectral imaging (HSI) to exploit data has been found in a wide variety of applications. This reprint book only presents a small glimpse of it. Many other important applications using HSI which have emerged in data exploitation are not covered in this reprint book. For example, such applications may include water pollution and toxic waste in environmental monitoring, pesticide residual detection in food safety and inspection, plant and crop disease detection in agriculture, tumor detection and breast cancer detection in medical imaging, drug traffic in law enforcement, etc. Nevertheless, this reprint book provides many techniques which may find their ways in these applications as well.

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Hardback

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Keywords

hyperspectral image few-shot classification; deep learning; meta-learning; relation network; convolutional neural network; constrained-target optimal index factor band selection (CTOIFBS); hyperspectral image; underwater spectral imaging system; underwater hyperspectral target detection; band selection (BS); constrained energy minimization (CEM); lightweight convolutional neural networks; deep learning; hyperspectral imagery classification; transfer learning; air temperature; spatial measurement; FTIR; MWIR; carbon dioxide absorption; target detection; coffee beans; insect damage; hyperspectral imaging; band selection; hyperspectral imaging; visualization; color formation models; hyperspectral image; multispectral image; image fusion; joint tensor decomposition; anomaly detection; constrained sparse representation; hyperspectral imagery; moving target detection; spatio-temporal processing; hyperspectral remote sensing; image classification; constraint representation; superpixel segmentation; multiscale decision fusion; hyperspectral imagery; plug-and-play; denoising; nonlinear unmixing; spectral reconstruction; residual augmented attentional u-shape network; spatial augmented attention; channel augmented attention; boundary-aware constraint; atmospheric transmittance; temperature; emissivity; separation; midwave infrared; hyperspectral images; hyperspectral image super-resolution; data fusion; spectral-spatial residual network; multispectral image; self-supervised training; hyperspectral; vegetation; generative adversarial network; deep learning; data augmentation; classification; rice leaf blast; hyperspectral imaging data; deep convolutional neural networks; fused features; evolutionary computation; heuristic algorithms; machine learning; unmanned aerial vehicles (UAVs); vegetation mapping; upland swamps; mine environment; rice; rice leaf folder; hyperspectral imaging; band selection; hyperspectral image classification; target detection; hyperspectral images; change detection; self-supervised learning; attention mechanism; hyperspectral image; multi-source image fusion; SFIM; least square estimation; spatial filter; hyperspectral image classification; hyperspectral imaging (HSI); hyperspectral target detection; hyperspectral reconstruction; hyperspectral unmixing