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■ This article presents an overview of the theoretical and practical issues associated with the development, analysis, and application of detection algorithms to exploit hyperspectral imaging data. We focus on techniques that exploit spectral information exclusively to make decisions regarding the type of each pixel—target or nontarget—on a pixel-by-pixel(More)
■ Spectral imaging for remote sensing of terrestrial features and objects arose as an alternative to high-spatial-resolution, large-aperture satellite imaging systems. Early applications of spectral imaging were oriented toward ground-cover classification, mineral exploration, and agricultural assessment, employing a small number of carefully chosen(More)
Characterization of the joint (among wavebands) probability density function (pdf) of hyperspectral imaging (HSI) data is crucial for several applications, including the design of constant false alarm rate (CFAR) detectors and statistical classifiers. HSI data are vector (or equivalently multivariate) data in a vector space with dimension equal to the(More)
Real-time detection and identification of man-made objects or materials (" targets ") from airborne platforms using hyper-spectral sensors are of great interest for civilian and military applications. Over the past several years, different algorithms for the detection of targets with known spectral signature have been developed. Most of these algorithms(More)
In this paper, we introduce a set of taxonomies that hierarchically organize and specify algorithms associated with hyperspectral unmixing. Our motivation is to collectively organize and relate algorithms in order to assess the current state-of-the-art in the field and to facilitate objective comparisons between methods. The hyperspectral sensing community(More)
The celebrated 1965 prediction by Gordon Moore regarding exponential improvements in integrated circuit density is so widely known, and has proven so accurate, that it has been elevated to the status of a " law ". Less appreciated is the fact that many areas of computation have benefited equally from progress in algorithms. In this paper we compare and(More)