Subpixel material identification by residual correlation

摘要

Abstract: The recognition of subpixel signatures is critical to realizing the full detection potential of multispectral and hyperspectral sensors. No approach has been developed that optimizes and fully characterizes the subpixel spectral components independently for every pixel in a data set. Such a full characterization is important because a target or material of interest may appear against a variety of background types in the same scene, and will undoubtedly be more distinguishable against some background types than others. Further, characterization of ground reflectance on a pixel-by-pixel basis is important for validating the quality of the atmospheric calibration results. We have developed an approach called the residual correlation method (RCM) for performing a full decomposition of each pixel into its component spectral elements. In this paper we describe preliminary results for the application of the RCM to hyperspectral pixel data. The work reported in this paper is from the first phase of a three phase research project. In this phase we develop the basic methodology for subpixel material identification and test it against hyperspectral data for a well-known area. The RCM determines the presence of minerals and gives a linear approximation of the abundances of the minerals in each pixel. PHase one performs a nominal atmospheric calibration using a simple normalization technique. The second phase will be to determine more precise mineral abundances using a nonlinear demixing approach based on the band shape of relevant absorption features. Phase two will also explore various methods of presenting the results of a full demixing for each pixel. Phase three of this research will be to perform a more rigorous atmospheric calibration and to include that approach as an intrinsic part of the RCM. !0