PolSAR Image Classification Based on Discriminative Clustering

Zhiqiang WEI; Haixia BI

doi:10.11999/JEIT180229

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2795-2803

Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229

Citation:

Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229

Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229

Citation:

Zhiqiang WEI, Haixia BI. PolSAR Image Classification Based on Discriminative Clustering[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2795-2803. doi: 10.11999/JEIT180229

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PolSAR Image Classification Based on Discriminative Clustering

doi: 10.11999/JEIT180229

Zhiqiang WEI^{1
,
,},
Haixia BI²

1.
Xi’an Electronic Engineering Research Institute, Xi’an 710100, China
2.
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Received Date: 2018-03-09
Rev Recd Date: 2018-08-22

Available Online: 2018-08-29

Publish Date: 2018-12-01

Abstract

Abstract

This paper presents a novel unsupervised image classification method for Polarimetric Synthetic Aperture Radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, an energy function is designed for unsupervised PolSAR image classification by combining a supervised Softmax Regression (SR) model with a Markov Random Field (MRF) smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, the classifiers and class labels are iteratively optimized by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. This approach is applied to real PolSAR benchmark data. Extensive experiments justify that the proposed approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.

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References(21)

References

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.

POTTIER E and SAILLARD J. On radar polarization target decomposition theorems with application to target classification by using network method[C]. Proceedings of the International Conference on Antennas and Propagation, York, UK, 1991, 1: 265–268.

FUKUDA S and HIROSAWA H. Support vector machine classification of land cover: Application to polarimetric SAR data[C]. Proceedings of the IEEE International Conference on Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia, 2001, 1: 187–189.

JIAO Licheng and LIU Fang. Wishart deep stacking network for fast PolSAR image classification[J]. IEEE Transactions on Image Processing, 2016, 25(7): 3273–3286 doi: 10.1109/TIP.2016.2567069

ZHOU Yu, WANG Haipeng, XU Feng, et al. Polarimetric SAR image classification using deep convolutional neural networks[J]. IEEE Geoscience Remote Sensing Letters, 2017, 13(12): 1935–1939 doi: 10.1109/LGRS.2016.2618840

GAO Wei, YANG Jian, and MA Wenting. Land cover classification for polarimetric SAR images based on mixture models[J]. Remote Sensing, 2014, 6(5): 3770–3790 doi: 10.3390/rs6053770

CLOUDE S R and POTTIER E. An entropy based classification scheme for land application of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68–78 doi: 10.1109/36.551935

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

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

XU Feng and JIN Yaqiu. Deorientation theory of polarimetric scattering targets and application to terrain surface classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(10): 2351–2364 doi: 10.1109/TGRS.2005.855064

DOULEGERIS A P, ANFINSEN S N, and ELROFT T. Classification with a non-Gaussian model for PolSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(10): 2999–3009 doi: 10.1109/TGRS.2008.923025

徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148 doi: 10.12000/JR16130

XU Feng, WANG Haipeng and JIN Yaqiu. Deep Learning as Applied in SAR Target Recognition and Terrain Classification[J]. Journal of Radars, 2017, 6(2): 136–148 doi: 10.12000/JR16130

钟能, 杨文, 杨祥立, 等. 基于混合Wishart模型的极化SAR图像非监督分类[J]. 雷达学报, 2017, 6(2): 136–148 doi: 10.12000/JR16133

ZHONG Neng, YANG Wen, YANG Xiangli, et al. Unsupervised Classification for Polarimetric Synthetic Aperture RadarImages Based on Wishart Mixture Models[J]. Journal of Radars, 2017, 6(2): 136–148 doi: 10.12000/JR16133

BACH F and HARCHAOUI Z. DIFFRAC: A discriminative and flexible framework for clustering[C]. Proceedings of Conference and Workshop on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2007: 49–56.

SUN Jian and PONECE J. Learning discriminative part detectors for image classification and cosegmentation[C]. Proceedings of IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013: 3400–3407.

ZHU Ciyou, BYRD R H, LU Peihuang, et al. L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization[J]. ACM Transaction Mathematical Software, 1997, 23(4): 550–560 doi: 10.1145/279232.279236

WU Yonghui, JI Kefeng, YU Wenxian, et al. Region-based classification of polarimetric SAR images using Wishart MRF[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(4): 668–672 doi: 10.1109/LGRS.2008.2002263

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

WANG Shuang, LIU Kun, PEI Jingjing, et al. Unsupervised classification of fully polarimetric SAR images based on scattering power entropy and copolarized ratio[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(3): 622–626 doi: 10.1109/LGRS.2012.2216249

LIU Gaofeng, LI Ming, WU Yan, et al. PolSAR image classification based on Wishart TMF with specific auxiliary field[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(7): 1230–1234 doi: 10.1109/LGRS.2013.2290066

LEE J S, GRUMES M R, POTTIER E, et al. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722–731 doi: 10.1109/TGRS.2003.819883

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