Local background estimation and the replacement target model

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.

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