Hyperspectral image analysis for material mapping using spectral matching

Recently, hyperspectral image analysis has obtained successful results in information extraction for earth remote sensing system. The data produced with this type of analysis is an important component of geographic databases. The domain of interest of such data covers a very large area of applications like target detection, pattern classification, material mapping and identification, etc. Material mapping techniques may be considered like multi-step target detection. Among the strategies for target detection, one of the most applied is the use of some similarity measures. In case of hyperspectral data, there are two general types of similarity measures: first are deterministic measures and second are stochastic measures. In this paper the deterministic measures for spectral matching are tested. These methods use some similarity measures like the euclidian distance (Ed), the spectral angle (SA), the Pearson spectral correlation (SC) and the spectral similarity value (SSV). In parallel, we have implemented a constrained energy minimizing (CEM) technique, for finding the most similar pixels on our materials of interest. These techniques are applied to two data sets which were taken with the Compact Airborne Spectrographic Imager (CASI), over the city of Toulouse in the South of France. Whereas each method has advantages and limits, a fusion technique is used to benefit from all the strong points and ignores the weak points of the methods. Results show that fusion may enhance the final target map; however, the primary algorithms are important and are useful for pure pixel targets.