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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery
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Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery

机译：基于克隆选择的高光谱图像降维

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摘要

A new stochastic search strategy inspired by the clonal selection theory in an artificial immune system is proposed for dimensionality reduction of hyperspectral remote-sensing imagery. The clonal selection theory is employed to describe the basic features of an immune response to an antigenic stimulus in order to meet the requirement of diversity in the antibody population. In our proposed strategy, dimensionality reduction is formulated as an optimization problem that searches an optimum with less number of features in a feature space. In line with this novel strategy, a feature subset search algorithm, clonal selection Feature-Selection (CSFS) algorithm, and a feature-weighting algorithm, Clonal-Selection Feature-Weighting (CSFW) algorithm, have been developed. In the CSFS, each solution is evolved in binary space, and the value of each bit is either 0 or 1, which indicates that the corresponding feature is either removed or selected, respectively. In CSFW, each antibody is directly represented by a string consisting of integer numbers and their corresponding weights. These algorithms are compared with the following four well-known algorithms: sequential forward selection, sequential forward floating selection, genetic-algorithm-based feature selection, and decision-boundary feature extraction using the hyperspectral remote-sensing imagery acquired by the Pushbroom Hyperspectral Imager and the Airborne Visible/Infrared Imaging Spectrometer, respectively. Experimental results demonstrate that CSFS and CSFW outperform other algorithms and hence provide effective new options for dimensionality reduction of hyperspectral remote-sensing imagery.
机译：提出了一种基于克隆选择理论的人工免疫系统随机搜索策略，以降低高光谱遥感影像的维数。为了满足抗体种群多样性的需要，采用了克隆选择理论来描述针对抗原刺激的免疫反应的基本特征。在我们提出的策略中，降维被公式化为一个优化问题，可以在特征空间中以较少的特征搜索最优值。根据该新颖策略，已经开发了特征子集搜索算法，克隆选择特征选择（CSFS）算法以及特征加权算法，克隆选择特征加权（CSFW）算法。在CSFS中，每个解决方案都是在二进制空间中演化的，并且每个位的值为0或1，这表示分别删除或选择了相应的功能。在CSFW中，每种抗体都直接由由整数及其相应权重组成的字符串表示。这些算法与以下四种著名算法进行了比较：顺序前向选择，顺序前向浮点选择，基于遗传算法的特征选择以及使用由Pushbroom Hyperspectral Imager和分别是机载可见/红外成像光谱仪。实验结果表明，CSFS和CSFW优于其他算法，因此为降低高光谱遥感影像的尺寸提供了有效的新选择。

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