Similarity Measures in Generating Spectrally Distinct Targets

Abstract

In multispectral and hyperspectral remote sensing, classification of pixels is obtained by means of spectral similarity of known field or library spectra to unknown image spectra. Endmember extraction is the most decisive task in hyperspectral image analysis. Endmember initialization algorithms (EIAs) play a key role and support endmember extraction algorithms (EEAs) in extracting near optimal set of endmembers. Though there are few endmember initialization techniques available, similarity measures are not explored in detail in target generation. Hence, in this paper, it is proposed to explore similarity measures in identifying spectrally distinct signatures to use them as initial endmembers. Individual similarity measures are combined to form hybrid similarity measures to confirm their effectiveness in generating spectrally distinct targets. Initial set of endmembers extracted by proposed algorithm are used for initializing classical EEA, the NFINDR, which is sensitive to endmember initialization, and their performance in final endmembers selection is verified. Experimental results on two hyperspectral data sets show the superior performance of the similarity based endmember initialization algorithm (SMEIA).