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Feature Selection Method Based on High-Resolution Remote Sensing Images and the Effect of Sensitive Features on Classification Accuracy

机译：基于高分辨率遥感影像的特征选择方法及敏感特征对分类精度的影响

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With the advent of high spatial resolution remote sensing imagery, numerous image features can be utilized. Applying a reasonable feature selection approach is critical to effectively reduce feature redundancy and improve the efficiency and accuracy of classification. This paper proposes a novel feature selection approach, in which ReliefF, genetic algorithm, and support vector machine (RFGASVM) are integrated to extract buildings. We adopt the ReliefF algorithm to preliminary filter high-dimensional features in the feature database. After eliminating the sorted features, the feature subset and the C and γ parameters of support vector machine (SVM) are encoded into the chromosome of the genetic algorithm. A fitness function is constructed considering the sample identification accuracy, the number of selected features, and the feature cost. The proposed method was applied to high-resolution images obtained from different sensors, GF-2, BJ-2, and unmanned aerial vehicles (UAV). The confusion matrix, precision, recall and F1-score were applied to assess the accuracy. The results showed that the proposed method achieved feature reduction, and the overall accuracy (OA) was more than 85%, with Kappa coefficient values of 0.80, 0.83 and 0.85, respectively. The precision of each image was more than 85%. The time efficiency of the proposed method was two-fold greater than SVM with all the features. The RFGASVM method has the advantages of large feature reduction and high extraction performance and can be applied in feature selection.
机译：随着高空间分辨率遥感影像的出现，可以利用许多影像特征。应用合理的特征选择方法对于有效减少特征冗余并提高分类效率和准确性至关重要。本文提出了一种新颖的特征选择方法，其中将ReliefF，遗传算法和支持向量机（RFGASVM）集成在一起以提取建筑物。我们采用ReliefF算法在特征数据库中初步过滤高维特征。消除排序后的特征后，将特征子集以及支持向量机（SVM）的C和γ参数编码到遗传算法的染色体中。考虑样本识别准确度，所选特征的数量和特征成本来构造适应度函数。所提出的方法应用于从不同传感器GF-2，BJ-2和无人机（UAV）获得的高分辨率图像。使用混淆矩阵，精度，召回率和F1分数来评估准确性。结果表明，该方法实现了特征约简，总体准确率（OA）达到85％以上，Kappa系数分别为0.80、0.83和0.85。每个图像的精度都超过85％。所提方法的时间效率是所有功能的SVM的两倍。 RFGASVM方法具有减少特征量大，提取性能高的优点，可用于特征选择。

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