Land Cover Classification of Quad-Polarimetric RADARSAT-2 SAR Image Based on Modified Subspace Method

AbstractCompared with conventional single-polarization synthetic aperture radar (SAR), quad-polarimetric SAR observations provide more information and show potential for land cover classification. In this study, we propose a modified subspace method (MOSM) for quad-polarimetric SAR image classification, which incorporates the advantages of the learning subspace method (LSM), the averaged learning subspace method (ALSM) and the multiple similarity method (MSM). To evaluate the recognition accuracy of the proposed method, we carry out experiments with quad-polarimetric RADARSAT-2 images for the Yellow River Delta area. We compare the results obtained using the MOSM algorithm with those obtained by the traditional supervised Wishart method. According to our analyses, the results obtained by the proposed method show a 10% higher accuracy rate than the results based on the classical supervised Wishart method, and the overall accuracy rate for the proposed method exceeds 87%, which indicates a good performance....