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| Citation: | Zou Huanxin, Luo Tiancheng, Zhang Yue, Zhou Shilin. Combined Conditional Random Fields Model for Supervised PolSAR Images Classification[J]. Journal of Radars, 2017, 6(5): 541-553. doi: 10.12000/JR16109
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Combined Conditional Random Fields Model for Supervised PolSAR Images Classification
DOI: 10.12000/JR16109
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Abstract
More features and contextual information can be extracted and exploited to improve classification accuracy in complex Polarimetric Synthetic Aperture Radar (PolSAR) imagery classification. However, the problems of overfitting and feature interference caused by the increased high dimensions of features lead to poor classification performance. To address these problems, a PolSAR image classification method based on combined Conditional Random Fields (CRF) is proposed in this paper. Unlike the traditional way of utilizing multiple feature information wherein multiple feature vectors are directly stacked to form a new one, combined CRF first forms multiple feature subsets according to different feature types and utilizes these feature subsets to train the same CRF model to obtain multiple child classifiers, thus obtaining multiple classification results. Then, the final classification result is gained by fusing multiple child classification results with the normalized overall classification accuracy of each classifier as the weight. Extensive experiments conducted on two real-world PolSAR images demonstrate that the accuracy of the proposed method is significantly improved than that of the single child classifier. For both the data sets used for performance evaluation, the classification accuracies of the proposed method increased by 13.38% and 11.55% than those of the method of stacking features, respectively, and by 13.78% and 14.75% than those of support vector machine-based method, respectively. -
References
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Jiang Tie-zhen, Xiao Wen-shu, Li Da-sheng, Liao Tong-qing. Feasibility Study on Passive-radar Detection of Space Targets Using Spaceborne Illuminators of Opportunity[J]. Journal of Radars, 2014, 3(6): 711-719. doi: 10.12000/JR14080
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- Figure 1. Flowchart of the combined CRF model for PolSAR images classification
- Figure 2. Two real-world PolSAR images for experiments
- Figure 3. Classification result of CRF model based on stacking features
- Figure 4. Classification results based on child classifiers using different features
- Figure 5. Classification result of combined CRF model
- Figure 6. Classification result of CRF model based on l-1 features selection
- Figure 7. Classification result of SVM model
- Figure 8. Classification result of combined CRF model based on majority voting
- Figure 9. Classification result of CRF model based on stacking features
- Figure 10. Classification results based on different child classifiers
- Figure 11. Classification result of combined CRF model
- Figure 12. Classification result of CRF model based on l-1 features selection
- Figure 13. Classification result of SVM model
- Figure 14. Classification result of CRF model based on majority voting