Abstract

Crop discrimination with synthetic aperture radar (SAR) data primarily depends on the characterization of crop geometry using radar backscatter response. Differences in phenological development of crops lead to dissimilar temporal signatures of backscatter intensities, which may influence the separability of the crop classes. This principle leads to multi-date classification approach. In this work, kernel principal component (KPCA) is adopted for feature selection from multi-date datasets, and the selected features are used for classification using support vector machine (SVM) classifier. The classification is investigated for both the KPCA-based SVM and only SVM approaches using quad-pol C-band RADARSAT-2 data acquired over the test site in Vijayawada, India. KPCA-based SVM classification shows an overall accuracy of 89%, which is better than 82% obtained using the SVM-based classification. The proposed methodology effectively incorporates the temporal crop information during classification.

摘要

使用合成孔径雷达 (SAR) 数据对作物进行鉴别主要取决于使用雷达后向散射响应对作物几何形状的表征。作物物候发育的差异导致后向散射强度的不同时间特征，这可能会影响作物类别的可分离性。这一原则导致了多日期分类方法。在这项工作中，采用核主成分（KPCA）从多日期数据集中进行特征选择，并使用支持向量机（SVM）分类器将选择的特征用于分类。对基于 KPCA 的 SVM 和仅使用在印度 Vijayawada 测试站点上获取的四极点 C 波段 RADARSAT-2 数据的 SVM 方法进行了分类研究。基于 KPCA 的 SVM 分类显示总体准确率为 89%，这优于使用基于 SVM 的分类获得的 82%。所提出的方法在分类过程中有效地结合了时间作物信息。