Polarimetric SAR (PolSAR) can comprehensively describe the differences of targets combining the polarimetric scattering power and the relative phase information among the polarimetric channels. For the traditional image classification methods, a single classifier cannot fully solve the classification problem, in which certain inevitable bias between the classification results and the actual condition exists. To improve the classification performance, ensemble learning theory has been introduced into PolSAR classification, in which a number of different learners are integrated to get the final results. However, a larger number of learners may also lead to some negative effects. The concept of selective ensemble learning (SEL) is then introduced, in which some better learners are selected from a group of individual learners according to the selection strategy and then integrated to obtain a generalized classifier. Based on this idea, a SEL-based PolSAR image classification method is proposed, in which the representative features are extracted based on the characteristics of PolSAR image meanwhile high-performing learners are selected using genetic algorithm optimization. In order to verify the effectiveness and application of the proposed SEL-based PolSAR image classification method, the tests are implemented using UAVSAR L-band PolSAR data, besides the other two newly acquired experimental data of Chinese airborne and spaceborne system. The quantitative and qualitative experimental results confirm that the proposed method has excellent performance in classification.
Polarimetric SAR obtains rich target scattering information by utilizing different polarizations to transmit and receive radar signals alternately, which has become an important tool for ground exploration. Presently, there are still some problems about the classification of PolSAR image because of the nonlinear data. Nonlinear features often lead the data difficult to distinguish in the conventional dimensions. Kernel method maps data to high-dimensional space, making the linearly inseparable data in the original dimension can be linearly separated in the high-dimensional space. Based on the study of the features of PolSAR data and signal sparse representation theory, this paper proposes a PolSAR image classification method based on kernel sparse representation, which optimizes the polarimetric and spatial information in PolSAR data, uses the kernel function method to solve the adverse effect of the nonlinear features on the classification results in the PolSAR image to obtain more accurate classification results. The experiment uses the fully polarimetric SAR data in San Francisco in the United States obtained by airborne AIRSAR, the advantages of kernel sparse representation in PolSAR image classification can be seen from the results.
Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.