Advances in Hyperspectral Data Exploitation

• Other Authors: Chang, Chein-I (Editor), Song, Meiping (Editor), Yu, Chunyan (Editor), Wang, Yulei (Editor), Yu, Haoyang (Editor), Li, Jiaojiao (Editor), Wang, Lin (Editor), Li, Hsiao-Chi (Editor), Li, Xiaorun (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
• Subjects: Technology: general issues; History of engineering & technology; 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; hyperspectral imagery classification; transfer learning; air temperature; spatial measurement; FTIR; MWIR; carbon dioxide absorption; target detection; coffee beans; insect damage; hyperspectral imaging; band selection; visualization; color formation models; 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; 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; self-supervised training; hyperspectral; vegetation; generative adversarial network; 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 image classification; change detection; self-supervised learning; attention mechanism; multi-source image fusion; SFIM; least square estimation; spatial filter; hyperspectral imaging (HSI); hyperspectral target detection; hyperspectral reconstruction; hyperspectral unmixing
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.