An efficient approach for dimension selection and classification in HSI images

Abstract

ABSTRACTIn this paper, we have proposed a divide and select approach for dimension selection (DSCS) and classification of hyperspectral images (HSI). The DSCS algorithm has been offered using a two-stage framework. In stage-1, we have generated the intermediate channels for every pair of spectral dimensions that are weakly correlated and highly significant. These channels have derived by splitting the adjacent dimensions without any loss of physical meaning. Intermediated channels are concatenated with original HSI in a better sense to transform the image into a more informative datacube. However, this leads to an increase in the dimensionality of the image. In stage-2, a trace and determinant based local feature response approach has applied to select the most informative dimensions of transformed HSI. We have exploited the local feature and scale selection methods to obtain significant channels. Finally, classification experiments have conducted for selected bands with SVM (Support Vector Machine) and LDA (Linear Discriminant Analysis). An expansion graph cut optimization has applied to improve the classification accuracy. This method has demonstrated as state-of-art for target detection in hyperspectral scenes.