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Palabras clave – provistas por la editorial

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