Proc. SPIE. 4381, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VII
KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Detection and tracking algorithms, Sensors, Image processing, Error analysis, Reflectivity, Chemical elements, Algorithm development, Binary data
Remote detection, identification, and quantification of materials is an important problem in earth resource assessment. Satellite-based hyperspectral imaging sensors currently being developed by government and industry partnerships (e.g. the Coastal Ocean Imaging Spectrometer aboard the Naval EarthMap Observer) appear to be uniquely qualified for this purpose. Obtaining accurate estimates of material abundance on a pixel-by-pixel basis poses many challenging algorithmic and computational difficulties. A significant issue that must be addressed is how to efficiently select endmembers from a library when that library is spectrally redundant. In this paper, we demonstrate how an improved version of the Residual Correlation Method (RCM+) can provide a flexible solution to this problem. The RCM+ offers a robust treatment for selecting endmembers from spectrally redundant libraries in a one-at-a-time fashion. We discuss alternative methods such as two-at-a-time, or more generally, N-at-a-time methods within a unified mathematical framework for analysis. Certain theorems apply to all such methods, and help to define a trade space for endmember selection methods in general. We demonstrate our results using synthetic test cases, and discuss how all endmember selection methods may be affected by redundancy within the library as well as specific properties of the data.
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.