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22 May 2014 On performance improvement of vertex component analysis based endmember extraction from hyperspectral imagery
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Abstract
Spectral mixture analysis is one of the major techniques in hyperspectral remote sensing image analysis. Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinct pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature similarity (e.g., Vertex Component Analysis) belong to this type. N-FINDR is a parallel-searching method, where all the endmembers are determined simultaneously. VCA is a sequential-searching method, finding endmembers one after another, which can greatly save computational cost. In this paper, we focus on VCA-based endmember extraction. In particular, we propose a new searching approach that makes the extracted endmembers more distinct. Real data experiments show that it can improve the quality of extracted endmembers.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qian Du, Nareenart Raksuntorn, and Nicolas H. Younan "On performance improvement of vertex component analysis based endmember extraction from hyperspectral imagery", Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240J (22 May 2014); https://doi.org/10.1117/12.2050701
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