Constrained subpixel target detection for remotely sensed imagery

By Chein-I. Chang, Daniel C. Heinz. IEEE Transactions on Geoscience and Remote Sensing, 2000

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Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization (2007, background): “Another metric uses an information theoretic measure, spectral information divergence (SID) [32].”
Anomaly detection and classification for hyperspectral imagery (2002, background): “As a result, it turns out to be a special case of the constrained energy minimization (CEM) filter developed in [6], [7], [9], [10] where the desired signal is designated as the unity vector.”
Urban surface water body detection with suppressed built-up noise based on water indices from Sentinel-2 MSI imagery (2018, background): “Under the assumption of low-probability targets in an image, the CEM detector can effectively distinguish the target of interest from the background (Ji et al., 2015; Chang and Heinz, 2000) using only the prior knowledge of the target (Marwaha et al., 2014).”
Retrieval of subpixel Tamarix canopy cover from Landsat data along the Forgotten River using linear and nonlinear spectral mixture models (2010, methodology): “Researchers have noted that the sum constraint (i) above is easy to implement, but the nonnegativity constraint (ii) is not (Chang & Heinz, 2000; Heinz & Chang, 2001; Hu et al., 1999).”
Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing (2012, background): “, they should be non-negative [6], and the sum of abundances”