Learn More
—Hyperspectral change detection has been shown to be a promising approach for detecting subtle targets in complex backgrounds. Reported change-detection methods are typically based on linear predictors that assume a space-invariant affine transformation between image pairs. Unfortunately, several physical mechanisms can lead to a significant space variance(More)
In hyperspectral unmixing, the objective is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels, into N constituent material spectra (or " endmembers ") with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing (i.e., joint estimation of endmembers and abundances)(More)
• Hyperspectral system capabilities for automated, real-time target detection are maturing – Significant focus has been military vehicle detection – Several airborne sensor systems have been demonstrated • Evolving applications are more demanding – Search and rescue: diverse, perhaps non-distinct targets – Asymmetric warfare: diverse, smaller, fleeting(More)
The goal of hyperspectral unmixing is to decompose an electromagnetic spectral dataset measured over M spectral bands and T pixels into N constituent material spectra (or “end-members”) with corresponding spatial abundances. In this paper, we propose a novel approach to hyperspectral unmixing based on loopy belief propagation (BP) that enables(More)
In this paper we consider the problem of classifying materials in a scene based on hyperspectral measurements and a known spectral library of intrinsic material reflectances. In addition to sensor noise, estimation of material reflectances is complicated by atmospheric distortion and local shadowing effects in the scene. This paper proposes a robust(More)
  • 1