Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation

This paper proposes a new hyperspectral unmixing method for nonlinearly mixed hyperspectral data using a semantic representation in a semisupervised fashion, assuming the availability of a spectral reference library. Existing semisupervised unmixing algorithms select members from an endmember library that are present at each of the pixels; most such methods assume a linear mixing model. However, those methods will fail in the presence of nonlinear mixing among the observed spectra. To address this issue, we develop an endmember selection method using a recently proposed semantic spectral representation obtained via nonhomogeneous hidden Markov chain model for a wavelet transform of the spectra. The semantic representation can encode spectrally discriminative features for any observed spectrum, and therefore, our proposed method can perform endmember selection without any assumption on the mixing model. The experimental results show that in the presence of sufficiently nonlinear mixing, our proposed method outperforms dictionary-based sparse unmixing approaches based on linear models.