关键词: 极化合成孔径雷达, 商空间理论, 粒度合成, 支持向量机, 图像分类

Abstract: Incomplete utilization of polarimetric information is one of the important factors that impact the result of polarimetric Synthetic Aperture Radar (SAR) image classification. In order to achieve the comprehensive utilization of polarimetric information, quotient space granularity composition theory, combined with multiple classifiers to construct different quotient space, was applied in classification of polarimetric SAR. Firstly, using different polarization decomposition method to get different characteristics, and based on these characteristics, setting different Support Vector Machine (SVM) classifiers to classify the image. Secondly, integrating these quotient spaces based on granularity composition theory to get more fine-grained result in order to achieve the upgrading of the classification accuracy. Finally, an experiment for AIRSAR image was given. The result shows the misclassification of targets is inhibited significantly and the classification accuracy of each class is improved.

Key words: polarimetry Synthetic Aperture Radar (SAR), quotient space theory, granularity composition, Support Vector Machine (SVM), image classification