基于邻域最小生成树的半监督极化SAR图像分类方法
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摘要
该文针对极化SAR图像分类中只有少量标记样本的问题,提出了一种基于邻域最小生成树的半监督极化SAR图像分类方法。该方法针对极化SAR图像以像素为分类对象的特点,结合自训练方法的思想,利用极化SAR图像像素点的空间信息,提出了基于邻域最小生成树辅助学习的样本选择策略,增加自训练过程中被选择无标记样本的可靠性,扩充标记样本数量,训练更好的分类器。最终用训练好的分类器对极化SAR图像进行测试。对3组真实的极化SAR图像进行测试,实验结果表明,该方法在只有少量标记样本的情况下能获得满意的分类结果,且分类正确率明显优于传统的分类算法。
In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree(NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar(PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.
In this paper, a novel semi-supervised classification method based on the Neighborhood Minimum Spanning Tree(NMST) is proposed to solve the Polarimetric Synthetic Aperture Radar(PolSAR) terrain classification when labeled samples are few. Combining the idea of self-training method and spatial information of the pixels in PolSAR image, a new help-training sample selection strategy based on spatial neighborhood information is proposed, named as NMST, to select the high reliable unlabeled samples to enlarge the training set and improve the base classifier. Finally, the PolSAR image is classified by this improved classifier. The experiments results tested on three PolSAR data sets show that the proposed method achieves a better performance than existing classification methods when the number of labeled samples is few.
引文
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[20]HUA Wenqiang,WANG Shuang,LIU Hongying,et al.Semisupervised PolSAR image classification based on improved cotraining[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(11):4971-4986.doi:10.1109/JSTARS.2017.2728067.
[21]GENG Jie,MA Xiaorui,FAN Jianchao,et al.Semisupervised classification of polarimetric SAR image via superpixel restrained deep neural network[J].IEEEGeoscience and Remote Sensing Letters,2018,15(1):122-126.doi:10.1109/LGRS.2017.2777450.
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[24]LEE J S,GRUNES M R,AINSWORTH T L,et al.Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2249-2258.doi:10.1109/36.789621.
[25]LEE J S,GRUNES M R,and DE GRANDI G.Polarimetric SAR speckle filtering and its implication for classification[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):363-373.doi:10.1109/36.789635.
[26]LONG Y,and LIU X.SVM lithological classification of PolSAR image in yushigou Area,Qilian Mountain[J].Scientific Journal of Earth Science,2013,3(4):128-132.
[27]LEE J S,GRUNES M R,and KWOK R.Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution[J].International Journal of Remote Sensing,1994,15(11):2299-2311.doi:10.1080/01431169408954244.
[2]HE Jinglu,WANG Yinghua,LIU Hongwei,et al.A novelautomatic PolSAR ship detection method based on superpixel-level local information measurement[J].IEEEGeoscience and Remote Sensing Letters,2018,15(3):384 -388.doi:10.1109/LGRS.2017.2789204.
[3]BUONO A,NUNZIATA F,MIGLIACCIO M,et al.Classification of the yellow river delta area using fully polarimetric SAR measurements[J].International Journal of Remote Sensing,2017,38(23):6714-6734.doi:10.1080/01431161.2017.1363437.
[4]RATHA D,BHATTACHARYA A,and FRERY A C.Unsupervised classification of PolSAR data using a scattering similarity measure derived from a geodesic distance[J].IEEE Geoscience and Remote Sensing Letters,2018,15(1):151-155.doi:10.1109/LGRS.2017.2778749.
[5]LI Dong and ZHANG Yunhua.Adaptive model-based classification of PolSAR data[J].IEEE Transactions on Geoscience and Remote Sensing,2018,56(12):6940-6955.doi:10.1109/TGRS.2018.2845944.
[6]HUANG Xiayuan,ZHANG Bo,QIAO Hong,et al.Local discriminant canonical correlation analysis for supervised PolSAR image classification[J].IEEE Geoscience and Remote Sensing Letters,2017,14(11):2102-2106.doi:10.1109/LGRS.2017.2752800.
[7]REDOLFI J,SáNCHEZ J,and FLESIA A G.Fishervectors for PolSAR image classification[J].IEEE Geoscience and Remote Sensing Letters,2017,14(11):2057-2061.doi:10 .1109/LGRS.2017.2750800.
[8]LIU Hongying,WANG Yikai,YANG Shuyuan,et al.Large polarimetric SAR data semi-supervised classification with spatial-anchor graph[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(4):1439-1458.doi:10.1109/JSTARS.2016.2518675.
[9]HUA W Q,WANG S,YANG Zhao et al.Semi-supervised PolSAR image classification based on improved Tritraining[C].2017 IEEE International Geoscience and Remote Sensing Symposium,Fort Worth,USA,2017:3937-3940.
[10]ROSENBERG C,HEBERT M,and SCHNEIDERMAN H.Semi-supervised self-training of object detection models[C].Proceedings of the 2005 7th IEEE Workshops on Applications of Computer Vision,Breckenridge,USA,2005:29-36.
[11]BLUM A and MITCHELL T.Combining labeled and unlabeled data with co-training[C].Proceedings of the 11th Conference on Computational Learning Theory,Madison,USA,1998:92-100.
[12]ZHU Zhihua and LI Ming.Tri-training:Exploiting unlabeled data using three classifiers[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(11):1529-1541.doi:10.1109/TKDE.2005.186.
[13]LIU Hongying,WANG Yikai,ZHU Dexiang,et al..Semisupervised classification based on anchor-spatial graph for large polarimetric SAR data[C].Proceedings of 2015 IEEEInternational Geoscience and Remote Sensing Symposium,Milan,Italy,2015:1845-1848.
[14]LIU Hongying,ZHU Dexiang,YANG Shuyuan,et al.Semisupervised feature extraction with neighborhood constraints for polarimetric SAR classification[J].IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing,2016,9(7):3001-3015.doi:10.1109/JSTARS.2016.2532922.
[15]WU Wenjin,LI Hailei,ZHANG Lu,et al.High-resolution PolSAR scene classification with pretrained deep convnets and manifold polarimetric parameters[J].IEEETransactions on Geoscience and Remote Sensing,2018,56(10):6159-6168.doi:10.1109/TGRS.2018.2833156.
[16]RASMUS A,VALPOLA H,HONKALA M,et al.Semisupervised learning with ladder networks[J].arXiv:1507.02672,2015.
[17]CHENG Yanhua,ZHAO Xin,CAI Rui,et al.Semisupervised multimodal deep learning for RGB-D object recognition[C].Proceedings of the 25th International JointConference on Artificial Intelligence,New York,USA,2016:3345-3351.
[18]H?NSCH R and HELLWICH O.Semi-supervised learning for classification of polarimetric SAR-data[C].Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium,Cape Town,South Africa,2009:987-990.
[19]Liu H Y,Wang Y K,Zhua D X et al..Semi-supervised classification based on anchor-spatial graph for large polarimetric SAR data[C].2015 IEEE International Geoscience and Remote Sensing Symposium,Milan,Italy,2015:1845-1848.
[20]HUA Wenqiang,WANG Shuang,LIU Hongying,et al.Semisupervised PolSAR image classification based on improved cotraining[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017,10(11):4971-4986.doi:10.1109/JSTARS.2017.2728067.
[21]GENG Jie,MA Xiaorui,FAN Jianchao,et al.Semisupervised classification of polarimetric SAR image via superpixel restrained deep neural network[J].IEEEGeoscience and Remote Sensing Letters,2018,15(1):122-126.doi:10.1109/LGRS.2017.2777450.
[22]LASZLO M and MUKHERJEE S.Minimum spanning tree partitioning algorithm for microaggregation[J].IEEETransactions on Knowledge and Data Engineer,2005,17(7):902-911.doi:10.1109/TKDE.2005.112.
[23]王晓东.计算机算法设计与分析[M].第4版,北京:电子工业出版社,2012:103-104.WANG Xiaodong.Design and Analysis of Algorithms[M].4th Ed,Beijing:China,Electronic Industry Press,2002:103-104.
[24]LEE J S,GRUNES M R,AINSWORTH T L,et al.Unsupervised classification using polarimetric decomposition and the complex Wishart classifier[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):2249-2258.doi:10.1109/36.789621.
[25]LEE J S,GRUNES M R,and DE GRANDI G.Polarimetric SAR speckle filtering and its implication for classification[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(5):363-373.doi:10.1109/36.789635.
[26]LONG Y,and LIU X.SVM lithological classification of PolSAR image in yushigou Area,Qilian Mountain[J].Scientific Journal of Earth Science,2013,3(4):128-132.
[27]LEE J S,GRUNES M R,and KWOK R.Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution[J].International Journal of Remote Sensing,1994,15(11):2299-2311.doi:10.1080/01431169408954244.
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