A ReliefF improved mRMR (RmRMR) criterion-based bag of visual words (BoVW) algorithm is proposed to filter the visual words that are generated with high information redundancy for remote sensing image classification. First, the contribution degree of each word to the classification is represented by its weighting parameter, which is assigned using the ReliefF algorithm. Next, the relevance and redundancy of each word are calculated according to the mRMR criterion with the addition of a dictionary balance coefficient. Finally, a novel dictionary discriminant function is established, and the globally discriminative small-scale dictionary subsets are filtered and obtained. Experimental results show that the proposed algorithm effectively reduces the amount of redundant information in the dictionary and better balances the relevance and redundancy of words to improve the feature descriptive power of dictionary subsets and markedly increase the classification precision on a high-resolution remote sensing image.

中文翻译：

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RmRMR增强型视觉词袋的高分辨率遥感影像分类

提出了一种基于ReliefF改进型mRMR（RmRMR）准则的视觉词袋（BoVW）算法，对具有高信息冗余度的视觉词进行过滤，以进行遥感图像分类。首先，每个单词对分类的贡献程度由其加权参数表示，该参数使用ReliefF算法分配。接下来，根据单词RMR的标准，加上字典平衡系数，计算每个单词的相关性和冗余度。最后，建立了新颖的字典判别函数，并对全局可判别的小规模字典子集进行了过滤和获取。