A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm

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• 作者：Lianru Gao ; Lina Zhuang ; Yuanfeng Wu ; Xu Sun ; Bing Zhang
• 关键词：Hyperspectral remote sensing ; Endmember extraction ; Discrete particle swarm optimization ; Preprocessing implementation
• 刊名：Soft Computing - A Fusion of Foundations, Methodologies and Applications
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：20
• 期：12
• 页码：4669-4683
• 全文大小：2,587 KB
• 刊物类别：Engineering
• 刊物主题：Numerical and Computational Methods in Engineering
  Theory of Computation
  Computing Methodologies
  Mathematical Logic and Foundations
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1433-7479
• 卷排序：20

文摘

Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.