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Random N-Finder (N-FINDR) Endmember Extraction Algorithms for Hyperspectral Imagery

机译:高光谱图像的随机N-Finder(N-FINDR)端元提取算法

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N-finder algorithm (N-FINDR) has been widely used in endmember extraction. When it comes to implementation several issues need to be addressed. One is determination of endmembers, $p$ required for N-FINDR to generate. Another is its computational complexity resulting from an exhaustive search. A third one is its requirement of dimensionality reduction. A fourth and probably the most critical issue is its use of random initial endmembers which results in inconsistent final endmember selection and results are not reproducible. This paper re-invents the wheel by re-designing the N-FINDR in such a way that all the above-mentioned issues can be resolved while making the last issue an advantage. The idea is to implement the N-FINDR as a random algorithm, called random N-FINDR (RN-FINDR) so that a single run using one set of random initial endmembers is considered as one realization. If there is an endmember present in the data, it should appear in any realization regardless of what random set of initial endmembers is used. In this case, the N-FINDR is terminated when the intersection of all realizations produced by two consecutive runs of RN-FINDR remains the same in which case the $p$ is then automatically determined by the intersection set without appealing for any criterion. In order to substantiate the proposed RN-FINDR custom-designed synthetic image experiments with complete knowledge are conducted for validation and real image experiments are also performed to demonstrate its utility in applications.
机译:N-finder算法(N-FINDR)已广泛用于端成员提取。在实施时,需要解决几个问题。一种是确定最终成员,即N-FINDR生成所需的$ p $。另一个是穷举搜索导致的计算复杂性。第三个是降低尺寸的要求。第四个也是最关键的问题是它使用随机的初始末端成员,这导致最终末端成员选择不一致,并且结果不可重现。本文通过重新设计N-FINDR来重新发明轮子,从而可以解决所有上述问题,同时使最后一个问题成为优势。这个想法是将N-FINDR实现为一种称为随机N-FINDR(RN-FINDR)的随机算法,以便将使用一组随机初始端成员的单次运行视为一种实现。如果数据中存在最终成员,则无论使用哪种初始最终成员集合,它都应以任何实现形式出现。在这种情况下,当两次连续运行RN-FINDR产生的所有实现的交集保持相同时,N-FINDR终止,在这种情况下,然后由交集自动确定$ p $,而无需考虑任何标准。为了证实提议的RN-FINDR,我们使用具有完整知识的定制设计合成图像实验进行了验证,并且还进行了真实图像实验以证明其在应用中的实用性。

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