Submit Manuscript
Peer Review
Editor Work
| 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
|
Combined Conditional Random Fields Model for Supervised PolSAR Images Classification
DOI: 10.12000/JR16109
-
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
[1] Zhang La-mei, Wang Xiao, Sun Liang-jie, et al.. Contextual sparse representation and its application in polarimetric SAR image classification[C]. IET International Radar Conference, Hangzhou, China, 2015: 1–5.[2] Shang Fang and Hirose A. Use of Poincare sphere parameters for fast supervised PolSAR land classification[C]. Proceedings of 2013 IEEE International Geoscience and Remote Sensing Symposium, Melbourne, VIC, 2013: 3175–3178.[3] Xie Wen, Jiao Li-cheng, and Zhao Jin. PolSAR image classification via D-KSVD and NSCT-Domain features extraction[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 227–231. doi: 10.1109/LGRS.2015.2506718[4] Kong J A, Swartz A A, Yueh H A, et al.. Identification of terrain cover using the optimum polarimetric classifier[J]. Journal of Electromagnetic Waves and Applications, 1988, 2(2): 171–194.[5] Silva W B, Freitas C C, Sant’Anna S J S, et al.. Classification of segments in PolSAR imagery by minimum stochastic distances between Wishart distributions[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3): 1263–1273. doi: 10.1109/JSTARS.2013.2248132[6] 孙即祥. 现代模式识别[M]. 长沙: 国防科技大学出版社, 2002: 385–447.Sun Ji-xiang. Modern Pattern Recognition[M]. Changsha: Press of National University of Defense Technology, 2002: 385–447.[7] Lee J S and Ainsworth T L. An overview of recent advances in Polarimetric SAR information extraction: Algorithms and applications[C]. Proceedings of 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, 2010: 851–854.[8] 钟平. 面向图像标记的随机场模型研究[D]. [博士论文], 国防科学技术大学, 2008.Zhong Ping. Random fields model for image label[D]. [Ph.D. dissertation], National University of Defense Technology, 2008.[9] Zhong Ping and Wang Run-sheng. A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12): 3978–3988. doi: 10.1109/TGRS.2007.907109[10] Lafferty J, McCallum A, and Pereira F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data[C]. Proceedings of the Eighteenth International Conference on Machine Learning, San Francisco, CA, USA, 2001: 282–289.[11] Kumar S and Hebert M. Discriminative random fields: A discriminative framework for contextual interaction in classification[C]. IEEE International Conference on Computer Vision, Piscataway, NJ, 2003: 1150–1157.[12] 吴立珍. 面向UAV战场感知的目标特征建模与应用研究[D]. [博士论文], 国防科学技术大学, 2012.Wu Li-zhen. Research on object feature modeling and applications for battlefield awareness of unmanned aerial vehicle[D]. [Ph.D. dissertation], National University of Defense Technology, 2012.[13] Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference[M]. California: Morgan Kaufmann, 1988: 247–289.[14] Wang Lei-guang, Dai Qin-ling, and Huang Xin. Spatial regularization of pixel-based classification maps by a two-step MRF method[C]. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, Beijing, 2016: 2407–2410.[15] Zhong Yan-fei, Zhao Ji, and Zhang Liang-pei. A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(11): 7023–7037. doi: 10.1109/TGRS.2014.2306692[16] Pieczynski W and Tebbache A N. Pairwise markov random fields and segmentation of textured images[J]. Machine Graphics and Vision, 2000, 9(3): 705–718.[17] Kumar S and Hebert M. A hierarchical field framework for unified context-based classification[C]. Proceedings of the Tenth IEEE International Conference on Computer Vision, Beijing, 2005: 1284–1291.[18] Jiang Wei, Chang S F, and Loui A C. Context-based concept fusion with boosted conditional random fields[C]. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, 2007: I-949–I-952.[19] Lee C H, Schmidt M, Murtha A, et al.. Segmenting brain tumors with conditional random fields and support vector machines[C]. Proceedings of the First International Conference on Computer Vision for Biomedical Image Applications, Beijing, China, 2005: 469–478.[20] Do T M T and Artières T. Polynomial conditional random fields for signal processing[C]. Proceedings of the 2006 Conference on ECAI 2006: 17th European Conference on Artificial Intelligence, Riva del Garda, Italy, 2006: 797–798.[21] Du Pei-jun, Samat A, Waske B, et al.. Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 38–53. doi: 10.1016/j.isprsjprs.2015.03.002[22] Zou Tong-yuan, Yang Wen, Dai Deng-xin, et al.. Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests[J]. EURASIP Journal on Advances in Signal Processing, 2010, 2010: Article No. 4.[23] Freeman A and Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963–973. doi: 10.1109/36.673687[24] Haralick R M, Shanmugam K, and Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621. doi: 10.1109/TSMC.1973.4309314[25] Yamaguchi Y, Moriyama T, Ishido M, et al.. Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699–1706. doi: 10.1109/TGRS.2005.852084 -
Proportional views
- Publishing Ethics
- Journal Insights
- Abstracting & Indexing
- Peer Review Policies
- Guide for Authors
- Conference
- ISSN 2095-283X (Print)ISSN 2097-339X (Online)
- CN 10-1030/TN
- CODEN LXEUAO
About Journal
- Sponsor: China Radio Detection and Ranging Industry Association (CRIA)
- Phone: 010-58887062
- Email：radars@aircas.ac.cn
- Publisher: Leida Xuebao Bianjibu (Editorial office of the Journal of Radars)
Contacts Us
京ICP备20021838号-8
Supported by: Beijing Renhe Information Technology Co. Ltd
Export File
Citation
Format
Content
DownLoad:
- 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