Context dependent hyperspectral subpixel target detection

A new class of subpixel target detection algorithms that use a local structured background model is introduced. Our approach, referred to as Context Dependent Target Detectors, extends existing structured detectors to multiple contexts. It is based on a robust context dependent spectral unmixing algorithm that uses multiple linear models to take into account the background variability. The claim is that robust context dependent unmixing provides a better description of the background with minimum target leakage, compared to global unmixing, and hence results in a better target-background separation. The approach is evaluated using the Orthogonal Subspace Projection (OSP) detector, the Adaptive Matched Subspace Detector (AMSD) and the Hybrid Subspace Detector (HSD). Experimental results show that our approach outperforms the baseline detectors (that use one global model for background).