Spectral unmixing-based post-processing for hyperspectral image classification
Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, possible connections between classification and spectral unmixing concepts have been rarely investigated. In this work, we propose a simple spectral unmixing-based post-processing method to improve the classification accuracies provided by supervised and semi-supervised techniques for hyperspectral image classification. The proposed approach exploits the information retrieved with spectral unmixing in order to complement the results obtained after the classification stage (which can be either supervised or semi-supervised), thus bridging the gap between unmixing and classification and exploiting both techniques in synergistic fashion for hyperspectral data interpretation. The proposed method is experimentally validated using a real hyperspectral data set collected by the Reflective Optics Spectrographic Imaging System (ROSIS). Our experimental results indicate that the proposed unmixing-based preprocessing can improve the classification results for some of the classes, particularly the most highly mixed ones, in supervised and semi-supervised scenarios using limited training samples.