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| Citation: | QIN Xianxiang, YU Wangsheng, WANG Peng, et al. Weakly supervised classification of PolSAR images based on sample refinement with complex-valued convolutional neural network[J]. Journal of Radars, 2020, 9(3): 525–538. doi: 10.12000/JR20062
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Weakly Supervised Classification of PolSAR Images Based on Sample Refinement with Complex-Valued Convolutional Neural Network
doi: 10.12000/JR20062
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Abstract
In this study, a weakly supervised classification method is proposed to classify the Polarimetric Synthetic Aperture Radar (PolSAR) images based on sample refinement using a Complex-Valued Convolutional Neural Network (CV-CNN) to solve the problem that the bounding-box labeled samples contain many heterogeneous components. First, CV-CNN is used for iteratively refining the bounding-box labeled samples, and the CV-CNN that can be used for direct classification is trained simultaneously. Then, the given PolSAR image is classified using the trained CV-CNN. The experimental results obtained using three actual PolSAR images demonstrate that the heterogeneous components can be effectively eliminated using the proposed method, obtaining significantly better classification results when compared with those obtained using the traditional fully supervised classification method in which original bounding-box labeled samples are used. Furthermore, the proposed method with CV-CNN is superior to those in which the classical Support Vector Machine(SVM) and Wishart classifier are used.
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References
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- Figure 1. Comparison illustration of pixel-level label and bounding-box label for a PolSAR data sample
- Figure 2. Flowchart of refining method for bounding-box labelled samples
- Figure 3. Illustration of architecture of CV-CNN
- Figure 4. Experimental image data 1
- Figure 5. Experimental image data 2
- Figure 6. Experimental image data 3
- Figure 7. Pauli-RGB image of the bounding-box labelled sample set of experimental data 1 and its classification results and refined pixel-level labels with three methods
- Figure 8. Pauli-RGB image of the bounding-box labelled sample set of experimental data 2 and its classification results and refined pixel-level labels with three methods
- Figure 9. Pauli-RGB image of the bounding-box labelled sample set of experimental data 3 and its classification results and refined pixel-level labels with three methods
- Figure 10. Curves of change rate of classification results on training set of experimental data 1
- Figure 11. Classification results of experimental data 1 by fully-supervised and proposed weakly-supervised methods
- Figure 12. Classification results of experimental data 2 by fully-supervised and proposed weakly-supervised methods
- Figure 13. Classification results of experimental data 3 by fully-supervised and proposed weakly-supervised methods