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Universality Of Wavelet-Based Non-Homogeneous Hidden Markov Chain Model Features For Hyperspectral Signatures, Siwei Feng, Marco Duarte, Mario Parente

Marco Duarte

Feature design is a crucial step in many hyperspectral signal processing applications like hyperspectral signature classification and unmixing, etc. In this paper, we describe a technique for automatically designing universal features of hyperspectral signatures. Universality is considered both in terms of the application to a multitude of classification problems and in terms of the use of specific vs. generic training datasets. The core component of our feature design is to use a non-homogeneous hidden Markov chain (NHMC) to characterize wavelet coefficients which capture the spectrum semantics (i.e., structural information) at multiple levels. Results of our simulation experiments show that the …