Application of spectral mixture analysis for terrain evaluation studies

Abstract

In this article, we describe an approach to calculate the spectral mixture within pixels and classify multispectral images. The results are compared with the classified images by traditional supervised rules such as Maximum Likelihood and appreciable results were accomplished. The method considers the number of endmembers that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. The only requirement for this method is a radiometrically corrected image because the endmembers are directly selected from the image. To make the method presented here more efficient, we propose to apply it only to the classes having low accuracy after a traditional supervised classification. Because the land cover classes in this study are related to a geomorphological terrain unit, we propose to mask the terrain units having problematic classes and decompose these units into their endmembers. A geomorphological analysis of the study area (Tonle Sap basin in Cambodia) was made to establish the relationship between land cover, landforms and soils through terrain mapping units. Then we performed a supervised classification of a Landsat Thematic Mapper (TM) image and of the same image merged with a SPOT-panchromatic (PAN) image, based on the land covers corresponding to the terrain mapping units. Then we masked a terrain unit having problematic spectral classes and applied the spectral mixture analysis which allowed an efficient separation of the land cover classes agglomerated in the preliminary classification. The result of this re-classification was re-inserted into the first classification and was compared statistically with the results obtained in the preliminary classification. We consider this procedure an efficient method to improve the results obtained from a supervised classification. The method can separate different land covers that were agglomerated in the preliminary segmentation. In our case, the classification accuracy for the terrain unit used (the fluvial terrace) increases from 62% (using only the TM bands) and 69% (using TM+ SPOT) to 83%.