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Classification and clustering perspective towards spectral unmxing

Spectral unmixing techniques decompose the pixels into constituent fractions in order to extract the subpixel information. This study reviews spectral unmixing techniques from a perspective different from earlier approaches in that the problem is studied from a classification as well as clustering perspective. In this research, we focus on addressing some core issues of spectral unmixing such as endmember variability, requirement of pure endmember values, and initialization sensitivity modelling. We propose a Support Vector Machine (SVM) based unmixing technique that incorporates endmember spectral variability. The method uses endmember extraction techniques to give optimal performance even in the absence of training samples. Further, our study presents an alternation of FCM based method for incorporating spectral variability, and the approach is found to be resilient to the brightness variation. An automatic approach for fuzziness parameter selection is also introduced. The sensitivity of FCM towards endmember initialization has been considerably reduced by optimizing the initial seed selection. The proposed approaches have been analyzed over various standard datasets.