Advances in Hyperspectral Data Exploitation

• Autore: Chang Chein-I
• Pubblicazione: Basel, : MDPI - Multidisciplinary Digital Publishing Institute, 2022
• Descrizione fisica: 1 online resource (434 p.)
• Soggetto topico: History of engineering & technology; Technology: general issues
• Soggetto non controllato: air temperature; anomaly detection; atmospheric transmittance; attention mechanism; band selection; band selection (BS); boundary-aware constraint; carbon dioxide absorption; change detection; channel augmented attention; classification; coffee beans; color formation models; constrained energy minimization (CEM); constrained sparse representation; constrained-target optimal index factor band selection (CTOIFBS); constraint representation; convolutional neural network; data augmentation; data fusion; deep convolutional neural networks; deep learning; denoising; emissivity; evolutionary computation; FTIR; fused features; generative adversarial network; heuristic algorithms; hyperspectral; hyperspectral image; hyperspectral image classification; hyperspectral image few-shot classification; hyperspectral image super-resolution; hyperspectral imagery; hyperspectral imagery classification; hyperspectral images; hyperspectral imaging; hyperspectral imaging (HSI); hyperspectral imaging data; hyperspectral reconstruction; hyperspectral remote sensing; hyperspectral target detection; hyperspectral unmixing; image classification; image fusion; insect damage; joint tensor decomposition; least square estimation; lightweight convolutional neural networks; machine learning; meta-learning; midwave infrared; mine environment; moving target detection; multi-source image fusion; multiscale decision fusion; multispectral image; MWIR; nonlinear unmixing; plug-and-play; relation network; residual augmented attentional u-shape network; rice; rice leaf blast; rice leaf folder; self-supervised learning; self-supervised training; separation; SFIM; spatial augmented attention; spatial filter; spatial measurement; spatio-temporal processing; spectral reconstruction; spectral-spatial residual network; superpixel segmentation; target detection; temperature; transfer learning; underwater hyperspectral target detection; underwater spectral imaging system; unmanned aerial vehicles (UAVs); upland swamps; vegetation; vegetation mapping; visualization
• Persona (resp. second.): SongMeiping; YuChunyan; WangYulei; YuHaoyang; LiJiaojiao; WangLin; LiHsiao-Chi; LiXiaorun; ChangChein-I
• Sommario/riassunto: 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.
• Titolo autorizzato: Advances in Hyperspectral Data Exploitation
• ISBN: 3-0365-5796-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910637782203321
• Lo trovi qui: Univ. Federico II