An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis

By Chein-I. Chang. IEEE Transactions on Information Theory, 2000

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Algorithms and applications for approximate nonnegative matrix factorization (2007, background): “This is known as the spectral unmixing problem in the hyperspectral imaging community (Chang, 2000; Keshava, 2003; Plaza et al., 2004).”
Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety (2014, background): “where X is a given hyperspectral pixel from the hyperspectral image; Y is another pixel with probability vector formed by matrix transformation from X [62].”
Vertex component analysis: a fast algorithm to unmix hyperspectral data (2005, methodology): “The symmetric Kullback distance [52], a relative entropy-based distance, is another error measure used to compare similarity between signatures, namely under the name spectral information divergence (SID) [53].”
Target-Adaptive CNN-Based Pansharpening (2017, methodology): “As an example, one may think to use typical measures employed for quality assessment or discrimination in multispectral or hyperspectral image analysis, such as the spectral angle mapper (SAM) [53] or the spectral information divergence (SID) [54].”
Hyperspectral and multispectral imaging for evaluating food safety and quality (2013, background): “13 demonstrates an example of using SID mapping to reduce the spectral dimension of the hyperspectral images.”