Experiments with Simplex ACE: dealing with highly variable targets

We investigate a constrained subspace detector that models the target spectrum as a positive linear combination of multiple reference spectra. This construction permits the input of a large number of target reference spectra, which enables us to go after even highly variable targets without being overwhelmed by false alarms. This constrained basis approach led to the derivation of both the simplex adaptive matched filter (Simplex AMF) and simplex adaptive cosine estimator (Simplex ACE) detectors. Our primary interest is in Simplex ACE, and as such, the experiments in this paper focus on evaluating the robustness of Simplex ACE (with Simplex AMF included for comparison). We present results using large spectral libraries implanted into real hyperspectral data, and compare the performance of our simplex detectors against their traditional subspace detector counterparts. In addition to a large (i.e., several hundred spectra) target library, we induce further target variability by implanting subpixel targets with both added noise and scaled illumination. As a corollary, we also show that in the limit as the target subspace approaches the image space, Subspace AMF becomes the RX anomaly detector.

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