Graph-based and statistical approaches for detecting spectrally variable target materials

Abstract

In discriminating target materials from background clutter in hyperspectral imagery, one must contend with variability in both. Most algorithms focus on the clutter variability, but for some materials there is considerable variability in the spectral signatures of the target. This is especially the case for solid target materials, whose signatures depend on morphological properties (particle size, packing density, etc.) that are rarely known a priori. In this paper, we investigate detection algorithms that explicitly take into account the diversity of signatures for a given target. In particular, we investigate variable target detectors when applied to new representations of the hyperspectral data: a manifold learning based approach, and a residual based approach. The graph theory and manifold learning based approach incorporates multiple spectral signatures of the target material of interest; this is built upon previous work that used a single target spectrum. In this approach, we first build an adaptive nearest neighbors (ANN) graph on the data and target spectra, and use a biased locally linear embedding (LLE) transformation to perform nonlinear dimensionality reduction. This biased transformation results in a lower-dimensional representation of the data that better separates the targets from the background. The residual approach uses an annulus based computation to represent each pixel after an estimate of the local background is removed, which suppresses local backgrounds and emphasizes the target-containing pixels. We will show detection results in the original spectral space, the dimensionality-reduced space, and the residual space, all using subspace detectors: ranked spectral angle mapper (rSAM), subspace adaptive matched filter (ssAMF), and subspace adaptive cosine/coherence estimator (ssACE). Results of this exploratory study will be shown on a ground-truthed hyperspectral image with variable target spectra and both full and mixed pixel targets.