We investigate the detection of opaque targets in cluttered multi/hyper-spectral imagery, using a local background estimation model. Unlike transparent "additive-model" targets (like gas-phase plumes), these are solid "replacement-model" targets, which means that the observed spectrum is a linear combination of the target signature and the background signature. Pixels with stronger targets are associated with correspondingly weaker backgrounds, and background estimators can over-estimate the background in a target pixel. In particular, "subtracting the background" (which generalizes the usual notion of subtracting the mean) to produce a residual image can actually have deleterious effect. We examine an adaptive partial background subtraction scheme, and evaluate its utility for the detection of replacement-model targets.
James Theiler and Amanda Ziemann, "Local background estimation and the replacement target model," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980V (Presented at SPIE Defense + Security: April 12, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262833.
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