Wavelet-Based Semantic Features for Hyperspectral Signature Discrimination
This work addresses material detection in hyperspectral imagery for remote sensing applications, but it appears incremental as it builds on existing statistical modeling techniques.
The paper tackles hyperspectral signature classification by applying non-homogeneous hidden Markov chain models to wavelet coefficients to capture structural information, and it reports that this approach outperforms existing methods in classification tasks.
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.