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首页> 外文期刊>Mobile networks & applications >Endmember Extraction of Hyperspectral Remote Sensing Images Based on an Improved Discrete Artificial Bee Colony Algorithm and Genetic Algorithm
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Endmember Extraction of Hyperspectral Remote Sensing Images Based on an Improved Discrete Artificial Bee Colony Algorithm and Genetic Algorithm

机译：基于改进的离散人工蜂殖民算法和遗传算法的基于改进的离散人工群核算和遗传算法的终结遥感图像提取

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摘要

Aiming at improving the performance of the endmember extraction problem in hyperspectral images, a new extraction method based on discrete hybrid artificial bee colony algorithm and genetic algorithm (DABC_GA) is proposed. By analyzing the characteristic of the problem, each dimension of candidate solution is a discrete and exclusive integer. Then we employ an optimization method with integral coding. By inheriting the strong exploration ability of the traditional artificial bee colony algorithm (ABC), we propose a discrete ABC which could quickly obtain more valuable endmembers combinations in the early stage. Then we select some outstanding results of DABC as the potential solutions of GA, which is adopted as another optimization tool in the later stage of iteration. The concept of complementary sets is proposed in the cross and mutation operators to guarantee the diversity and completeness of solutions. Meanwhile, the greedy strategy is adopted to ensure that the favorable potential solutions are not discarded. Compared with conventional extraction algorithms in simulated and real hyperspectral remote sensing data, the experimental results show the validity of our proposed algorithm.
机译：旨在提高高光谱图像中的终点提取问题的性能，提出了一种基于离散混合人造菌落算法和遗传算法（DABC_GA）的新提取方法。通过分析问题的特征，每个候选解决方案的维度是一个离散和独占整数。然后我们采用具有积分编码的优化方法。通过继承传统人造群群算法（ABC）的强大勘探能力，我们提出了一个离散的ABC，这可以在早期获得更有价值的终点。然后我们选择DABC的一些优异结果作为GA的潜在解决方案，在迭代后期的阶段被用作另一个优化工具。互补集的概念是在交叉和突变运营商中提出的，以保证解决方案的多样性和完整性。同时，采用了贪婪的策略来确保不丢弃有利的潜在解决方案。与模拟和实际高光谱遥感数据中的传统提取算法相比，实验结果表明了我们所提出的算法的有效性。

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