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Title: Improving spatial- spectral endmember extraction in the presence of anomalous ground objects
Authors: Mei, S
He, M
Zhang, Y
Wang, Z
Feng, D
Keywords: Anomalous ground objects
Endmember extraction (EE)
Endmember identification
Hyperspectral remote sensing
Spectral mixture analysis (SMA)
Issue Date: 2011
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2011, v. 49, no. 11, p. 4210-4222 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial-spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend to neglect anomalous endmembers that are also of interest. Therefore, in this paper, an improved SSEE (iSSEE) algorithm is proposed to address such limitation of conventional SSEE algorithms by accounting for both anomalous and normal endmembers. By developing simplex projection and simplex complementary projection, all the hyperspectral pixels are projected into a simplex determined by the normal endmembers extracted in conventional SSEE algorithms. As a result, anomalous endmembers are identified iteratively by utilizing the l2∞ norm to find the maximum simplex complementary projection. In order to determine how many anomalous endmembers are to be extracted, a novel Residual-be-Noise Probability-based algorithm is also proposed by elegantly utilizing the spatial-purity map generated in the previous SSEE step. Experimental results on both synthetic and real datasets demonstrate that simplex projection errors can be significantly reduced by identifying both anomalous and normal endmembers in the proposed iSSEE algorithm. It is also confirmed that the performance of the proposed iSSEE algorithm clearly outperforms that of SEE algorithms since both spatial context and spectral similarity are utilized.
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2011.2163160
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