I -- Introduction -- 1.1 -- Hyperspectral and multispectral imaging: setting the scene -- 1.2 -- Configuration of hyperspectral and multispectral imaging systems -- II -- Algorithms and methods -- 2.1 -- Preprocessing of hyperspectral and multispectral images -- 2.2 -- Hyperspectral compression -- 2.3 -- Pansharpening -- 2.4. Unsupervised exploration of hyperspectral and multispectral images -- 2.5 -- Multivariate curve resolution for hyperspectral image analysis -- 2.6 -- Nonlinear spectral unmixing -- 2.7 -- Variability of the endmembers in spectral unmixing -- 2.8 -- An overview of regression methods in hyperspectral and multispectral imaging -- 2.9 -- Classical least squares for detection and classification -- 2.10 -- Supervised classification methods in hyperspectral imaging--recent advances -- 2.11 -- Fusion of hyperspectral imaging and LiDAR for forest monitoring -- 2.12 -- Hyperspectral time series analysis: hyperspectral image data streams interpreted by modeling known and unknown variations -- 2.13 -- Statistical biophysical parameter retrieval and emulation with Gaussian processes -- III -- Application fields -- 3.1 -- Applications in remote sensing--natural landscapes -- 3.2 -- Applications in remote sensing--anthropogenic activities -- 3.3 -- Hyperspectral imaging in crop fields: precision agriculture -- 3.4 -- Food and feed production -- 3.5 -- Hyperspectral imaging for food-related microbiology applications -- 3.6 -- Hyperspectral imaging in medical applications -- 3.7. Hyperspectral imaging as a part of pharmaceutical product design -- 3.8 -- Hyperspectral imaging for artworks investigation -- 3.9 -- Growing applications of hyperspectral and multispectral imaging
