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Automatic Algorithms for Endmember Extraction

机译：自动提取端成员的算法

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Endmenber extraction has received increasing interests in hyperspectral image analysis. Two major issues are of interest. One is determination of endmembers, p, required to be generated and the other is generation of initial endmembers. Since most endmember extraction algorithms (EEAs) use randomly generated vectors as their initial endmembers to initialize their algorithms, their final generated endmembers are generally determined by these random initial endmembers. As a result, a different set of random initial endmembers may well likely produce a different final set of desired endmembers. This paper converts this disadvantage to an advantage and further resolves the above-mentioned two issues. Due to the random nature of initial endmembers, the proposed idea is to implement an EEA as a random algorithm so that a single run using a random set of initial endmembers is considered as a realization of a random algorithm. As a result, if an EEA is implemented several times with different sets of random initial endmembers, the intersection of their final generated endmembers in all runs should contain the desired endmembers. An EEA is then terminated when their produced intersections converge to the same set of desired endmembers. In this case, there is no need to determine the p. An EEA implemented in such a manner is called automatic EEA (AEEA). Two commonly used EEAs, pixel purity index (PPI) and N-finder algorithm (N-FINDR), are extended to AEEAs along with a new automatic ICA-based EEA. Experimental results demonstrate that the AEEA performs at least as well as their counterparts.
机译：Endmenber提取在高光谱图像分析中受到越来越多的关注。有两个主要问题。一种是确定需要生成的端成员p，另一种是生成初始端成员。由于大多数端成员提取算法（EEA）使用随机生成的向量作为其初始端成员来初始化其算法，因此通常由这些随机初始端成员确定其最终生成的端成员。结果，不同组的随机初始末端成员很可能产生所需最终成员的不同最终集合。本文将这一缺点转化为优势，并进一步解决了上述两个问题。由于初始端成员的随机性，提出的想法是将EEA实现为随机算法，以便将使用初始端成员的随机集的单次运行视为随机算法的实现。结果，如果用不同组的随机初始端成员多次实施一次EEA，则其最终生成的端成员在所有运行中的交集都应包含所需的端成员。然后，当它们产生的相交点收敛到同一组所需的最终成员时，终止EEA。在这种情况下，不需要确定p。以这种方式实现的EEA称为自动EEA（AEEA）。两种常用的EEA，即像素纯度指数（PPI）和N-finder算法（N-FINDR），连同新的基于ICA的自动EEA一起扩展到了AEEA。实验结果表明，AEEA的性能至少与同类产品相同。

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