A Classification Feature Optimization Method for Remote Sensing Imagery Based on Fisher Score and mRMR

文献类型： 外文期刊

第一作者： Lv, Chengzhe

作者： Lv, Chengzhe;Lu, Yuefeng;Feng, Xinyi;Fan, Huadan;Xu, Changqing;Lu, Yuefeng;Lu, Yuefeng;Lu, Miao;Xu, Lei

作者机构：

关键词： object-oriented; feature selection; Fisher Score; mRMR

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.838； 五年影响因子：2.921 ）

ISSN：

年卷期： 2022 年 12 卷 17 期

页码：

收录情况： SCI

摘要： In object-oriented remote sensing image classification experiments, the dimension of the feature space is often high, leading to the "dimension disaster". If a reasonable feature selection method is adopted, the classification efficiency and accuracy of the classifier can be improved. In this study, we took GF-2 remote sensing imagery as the research object and proposed a feature dimension reduction algorithm combining the Fisher Score and the minimum redundancy maximum relevance (mRMR) feature selection method. First, the Fisher Score was used to construct a feature index importance ranking, following which the mRMR algorithm was used to select the features with the maximum correlation and minimum redundancy between categories. The feature set was optimized using this method, and remote sensing images were automatically classified based on the optimized feature subset. Experimental analysis demonstrates that, compared with the traditional mRMR, Fisher Score, and ReliefF methods, the proposed Fisher Score-mRMR (Fm) method provides higher accuracy in remote sensing image classification. In terms of classification accuracy, the accuracy of the Fm feature selection method with RT and KNN classifiers is improved compared with that of single feature selection method, reaching 95.18% and 96.14%, respectively, and the kappa coefficient reaches 0.939 and 0.951, respectively.

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