Abstract Target identification using Remote Sensing techniques saves time, cost and reduces difficulties in field investigation. The endmember is a reference spectral response of a pure pixel in the hyperspectral image and is used for object identification/classification from hyperspectral data. Quality of endmembers selected influences classification accuracy. Though there have been several algorithms proposed for endmember extraction, choosing a benchmark algorithm requires further investigation. To the best of our knowledge, similarity measures have not been explored much in the extraction of spectrally distinct signatures called endmembers. In this paper, we propose a similarity measures based subtractive clustering algorithm (SM-SCA) for endmember extraction. The objective of this paper is to explore the applicability of a SM-SCA and effectiveness of different similarity measures in endmember extraction and to compare it’s performance with classical endmember extraction algorithms. Implementation on airborne hyperspectral (Samson data and AVIRIS data over Cuprite region) and synthetic data proves that SM-SCA is capable of extracting endmembers of all the materials identified in the data, with appropriate similarity measure. Experimental results show that (i) the similarity measures are potential not only to discriminate but also in extraction of different endmember signatures and (ii) the proposed SM-SCA with phase correlation similarity measure perform comparable to the classical endmember extraction algorithms in identifying endmembers.

中文翻译：

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端元提取的减法聚类和相位相关相似性测度

摘要 利用遥感技术进行目标识别可以节省时间、成本并减少野外调查的难度。端元是高光谱图像中纯像素的参考光谱响应，用于从高光谱数据中识别/分类对象。所选端元的质量影响分类精度。尽管已经提出了几种用于端元提取的算法，但选择基准算法需要进一步研究。据我们所知，在被称为端元的光谱不同特征的提取中，相似性度量还没有得到太多探索。在本文中，我们提出了一种基于相似性度量的减法聚类算法（SM-SCA）用于端元提取。本文的目的是探索 SM-SCA 的适用性和不同相似性度量在端元提取中的有效性，并将其与经典端元提取算法的性能进行比较。机载高光谱（Samson 数据和铜矿区域的 AVIRIS 数据）和合成数据的实施证明，SM-SCA 能够以适当的相似性度量提取数据中识别出的所有材料的端元。实验结果表明（i）相似性度量不仅可以区分而且在提取不同的端元签名方面具有潜力，并且（ii）所提出的具有相位相关相似性度量的 SM-SCA 在识别端元方面的性能与经典端元提取算法相当。