A spectral unmixing method based on wavelet weighted similarity

With the rapid development of the hyperspectral technology, spectra unmixing has received more and more attentions. In this paper, in order to measure the similarity between the extracted endmember spectra and the actual corresponding spectra of land covers and maintain the spectral absorption feature, a wavelet weighted similarity is presented. And by introducing it into the minimum distance constrained nonnegative matrix factorization method, a spectral unmixing method based on the wavelet weighted similarity is proposed. Base on the experiments with real hyperspectral image, the feasibility and real performance of our method has been examined and compared with that of unsupervised spectral unmixing method.