Universality of Wavelet-Based Non-Homogeneous Hidden Markov Chain Model Features for Hyperspectral Signatures

Siwei Feng, Marco F. Duarte, Mario Parente; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 19-27

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 designed features meet our expectation in terms of universality.