  Hyperspectral signature classification is a quantitative analysis approach
for hyperspectral imagery which performs detection and classification of the
constituent materials at the pixel level in the scene. The classification
procedure can be operated directly on hyperspectral data or performed by using
some features extracted from the corresponding hyperspectral signatures
containing information like the signature's energy or shape. In this paper, we
describe a technique that applies non-homogeneous hidden Markov chain (NHMC)
models to hyperspectral signature classification. The basic idea is to use
statistical models (such as NHMC) to characterize wavelet coefficients which
capture the spectrum semantics (i.e., structural information) at multiple
levels. Experimental results show that the approach based on NHMC models can
outperform existing approaches relevant in classification tasks.
