Abstract
Spectral unmixing is a popular technique for hyperspectral data interpretation. It focuses on estimating the abundance of pure spectral signature (called as endmembers) in each observed image signature. However, the identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of endmembers in a given scene. To deal with these problems, the sparsity-based unmixing algorithms, which regard a large standard spectral library as endmembers, have recently been proposed. However, the high mutual coherence of spectral libraries always affects the performance of sparse unmixing. In addition, the hyperspectral image has the special characteristics of space. In this paper, a new unmixing algorithm via low-rank representation (LRR) based on space consistency constraint and spectral library pruning is proposed. The algorithm includes the spatial information on the LRR model by means of the spatial consistency regularizer which is based on the assumption that: it is very likely that two neighbouring pixels have similar fractional abundances for the same endmembers. The pruning strategy is based on the assumption that, if the abundance map of one material does not contain any large values, it is not a real endmember and will be removed from the spectral library. The algorithm not only can better capture the spatial structure of data but also can identify a subset of the spectral library. Thus, the algorithm can achieve a better unmixing result and improve the spectral unmixing accuracy significantly. Experimental results on both simulated and real hyperspectral datasets demonstrate the effectiveness of the proposed algorithm.
Highlights
Hyperspectral imaging (HSI) has gained more attention in the past two decades
Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation [1]
In this paper, we propose a new hyperspectral unmixing method based on low-rank representation (LRR) model with dictionary pruning strategy
Summary
Hyperspectral imaging (HSI) has gained more attention in the past two decades. Spectral unmixing is an important task for remotely sensed hyperspectral data exploitation [1]. The identification of the endmembers in the original hyperspectral data becomes a challenge due to the lack of pure pixels in the scenes and the difficulty in estimating the number of endmembers in a given scene To deal with these problems, the sparsity-based unmixing algorithms [17], which regard a large standard spectral library as endmembers, have recently been proposed. CLSUnSAL exploits the fact that a hyperspectral image always contains a small number of endmembers so that the fractional abundances matrix of the spectral library signatures contains only a few lines with nonzero entries Both SUnSAL-TV and CLSUnSAL have performed better than the traditional sparse-based unmixing methods. Based on the dictionary pruning strategy, the proposed algorithm can use the spectral library as endmembers and avoid extracting them from the hyperspectral image, compared with the algorithm in [36]. N is the number of signals, L is the signal dimension, m is the number of dictionary atoms
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