Skin-Based Hyperspectral Dismount Detection Using Sparse Representation

This paper presents a sparsity-based dismount detection algorithm for hyperspectral skin signatures in conjunction with the sequential forward feature selection (SFFS) scheme. The proposed sparsity-based detection (SD) approach relies on the observation that spectral signatures belonging to the same class approximately lie in a low-dimensional subspace. An unknown test sample can be represented by only a few training samples in the structured dictionary, and the underlying sparse representation vector contains discriminative information for detection. The proposed algorithm is applicable to both spectrally pure as well as mixed pixels. Experimental results show that the SD approach outperforms classical hyper spectral detection algorithms such as the adaptive coherence estimation (ACE) algorithm, orthogonal subspace projection (OSP), and the adaptive matched subspace detector (AMSD).