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| Citation: | DENG Shasa, ZHANG Fan, YIN Qiang, et al. Refined ship feature characterization method of full-polarimetric synthetic aperture radar for visual interpretation[J]. Journal of Radars, 2024, 13(2): 374–395. doi: 10.12000/JR23078
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Refined Ship Feature Characterization Method of Full-polarimetric Synthetic Aperture Radar for Visual Interpretation
DOI: 10.12000/JR23078
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Abstract
With advances in satellite technology, Polarimetric Synthetic Aperture Radar (PolSAR) now have higher resolution and better data quality, providing excellent data conditions for the refined visual interpretation of artificial targets. The primary method currently used is a multicomponent decomposition, but this method can result in pixel misdivision problems. Thus, we propose a non-fixed threshold division method for achieving advanced feature ship structure characterization in full-polarimetric SAR images. Yamaguchi decomposition can effectively identify the primary scattering mechanism and characterize artificial targets. Its modified volume scattering model is more consistent with actual data. The polarization entropy can serve as the target scattering mechanism at a specified equivalent point in the weakly depolarized state, which can effectively highlight the ship structure. This paper combines the three components of the Yamaguchi decomposition algorithm with the entropy, and divides it into a nine-classification plane with a non-fixed threshold. This method reduces category randomness generated by noise at the threshold boundary for complicated threshold treatments. Furthermore, the Mixed Scattering Mechanism (MSM) which is the region where both secondary scattering and single scattering are significant, was proposed to better match the scattering types of typical structures of vessels in the experiment. The Generalized Similarity Parameter (GSP) was used to further shorten the intra-class distance and perform iterative clustering using a modified GSP-Wishart classifier. This method improves the vessel distinguishability by enhancing the secondary and mixed scattering mechanisms. Finally, this paper uses full-polarimetric SAR data from a port in Shanghai, China, for the experiment. We collected and filtered ship information and optical data from this port through the Automatic Identification System (AIS) and matched them with the ships in full-polarimetric SAR images to verify the correct characterization of each vessel’s features. The experimental results show that the proposed method can effectively distinguish three types of vessels: bulk carriers, container ships and tankers. -
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References
[1] 张杰, 张晰, 范陈清, 等. 极化SAR在海洋探测中的应用与探讨[J]. 雷达学报, 2016, 5(6): 596–606. doi: 10.12000/JR16124.ZHANG Jie, ZHANG Xi, FAN Chenqing, et al. Discussion on application of polarimetric synthetic aperture radar in marine surveillance[J]. Journal of Radars, 2016, 5(6): 596–606. doi: 10.12000/JR16124.[2] 代大海, 廖斌, 肖顺平, 等. 雷达极化信息获取与处理的研究进展[J]. 雷达学报, 2016, 5(2): 143–155. doi: 10.12000/JR15103.DAI Dahai, LIAO Bin, XIAO Shunping, et al. Advancements on radar polarization information acquisition and processing[J]. Journal of Radars, 2016, 5(2): 143–155. doi: 10.12000/JR15103.[3] 赵春雷, 王亚梁, 阳云龙, 等. 雷达极化信息获取及极化信号处理技术研究综述[J]. 雷达学报, 2016, 5(6): 620–638. doi: 10.12000/JR16092.ZHAO Chunlei, WANG Yaliang, YANG Yunlong, et al. Review of radar polarization information acquisition and polarimetric signal processing techniques[J]. Journal of Radars, 2016, 5(6): 620–638. doi: 10.12000/JR16092.[4] SATO A, YAMAGUCHI Y, SINGH G, et al. Four-component scattering power decomposition with extended volume scattering model[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(2): 166–170. doi: 10.1109/LGRS.2011.2162935.[5] CHEN Siwei, WANG Xuesong, XIAO Shunping, et al. General polarimetric model-based decomposition for coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(3): 1843–1855. doi: 10.1109/TGRS.2013.2255615.[6] XI Yuyang, LANG Haitao, TAO Yunhong, et al. Four-component model-based decomposition for ship targets using PolSAR data[J]. Remote Sensing, 2017, 9(6): 621. doi: 10.3390/rs9060621.[7] SINGH G and YAMAGUCHI Y. Model-based six-component scattering matrix power decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5687–5704. doi: 10.1109/TGRS.2018.2824322.[8] SINGH G, MALIK R, MOHANTY S, et al. Seven-component scattering power decomposition of POLSAR coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8371–8382. doi: 10.1109/TGRS.2019.2920762.[9] PAN Xueli, WU Zhenhua, YANG Lixia, et al. Ship detection method based on scattering contribution for PolSAR image[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4503205. doi: 10.1109/LGRS.2021.3138796.[10] 全斯农, 范晖, 代大海, 等. 一种基于精细极化目标分解的舰船箔条云识别方法[J]. 雷达学报, 2021, 10(1): 61–73. doi: 10.12000/JR20123.QUAN Sinong, FAN Hui, DAI Dahai, et al. Recognition of ships and chaff clouds based on sophisticated polarimetric target decomposition[J]. Journal of Radars, 2021, 10(1): 61–73. doi: 10.12000/JR20123.[11] LIU Dongsheng and HAN Ling. Integration of fine model-based decomposition and guard filter for ship detection in PolSAR images[J]. Sensors, 2021, 21(13): 4295. doi: 10.3390/s21134295.[12] QUAN Sinong, ZHANG Tao, WANG Wei, et al. Exploring fine polarimetric decomposition technique for built-up area monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5204719. doi: 10.1109/TGRS.2023.3257773.[13] CUI Xingchao, SU Yi, and CHEN Siwei. A saliency detector for polarimetric SAR ship detection using similarity test[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(9): 3423–3433. doi: 10.1109/JSTARS.2019.2925833.[14] LIN Huiping, WANG Hongmiao, WANG Jing, et al. A novel ship detection method via generalized polarization relative entropy for PolSAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4001205. doi: 10.1109/LGRS.2020.3019196.[15] 殷君君, 彭嘉耀, 杨健, 等. 基于局部竞争策略的极化SAR图像精细分类[J]. 雷达科学与技术, 2021, 19(5): 499–508, 516. doi: 10.3969/j.issn.1672-2337.2021.05.005.YIN Junjun, PENG Jiayao, YANG Jian, et al. Refined polarimetric SAR image classification based on localized competition[J]. Radar Science and Technology, 2021, 19(5): 499–508, 516. doi: 10.3969/j.issn.1672-2337.2021.05.005.[16] XING Xiangwei, JI Kefeng, ZOU Huanxin, et al. Ship classification in TerraSAR-X images with feature space based sparse representation[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1562–1566. doi: 10.1109/LGRS.2013.2262073.[17] 张晰, 张杰, 纪永刚, 等. 基于结构特征的SAR船只类型识别能力分析[J]. 海洋学报, 2010, 32(1): 146–152.ZHANG Xi, ZHANG Jie, JI Yonggang, et al. The capability analysis of ship classification by structure feature using SAR images[J]. Acta Oceanologica Sinica, 2010, 32(1): 146–152.[18] LANG Haitao, ZHANG Jie, ZHANG Xi, et al. Ship classification in SAR image by joint feature and classifier selection[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(2): 212–216. doi: 10.1109/LGRS.2015.2506570.[19] WANG Juan, HUANG Weigen, YANG Jingsong, et al. Polarization scattering characteristics of some ships using polarimetric SAR images[C]. SPIE 8179, SAR Image Analysis, Modeling, and Techniques XI, Prague, Czech Republic, 2011: 265–271.[20] LUO M R, CUI G, and RIGG B. The development of the CIE 2000 colour-difference formula: CIEDE2000[J]. Color Research &Application, 2001, 26(5): 340–350. doi: 10.1002/col.1049.[21] CLOUDE S R and POTTIER E. An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68–78. doi: 10.1109/36.551935.[22] 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.[23] AN Wentao, ZHANG Weijie, YANG Jian, et al. On the similarity parameter between two targets for the case of multi-look polarimetric SAR[J]. Chinese Journal of Electronics, 2009, 18(3): 545–550. doi: 10.23919/CJE.2009.10138264.[24] 张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049.ZHANG Qingjun. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269–277. doi: 10.11947/j.AGCS.2017.20170049.[25] 周伟, 孙艳丽, 许成斌, 等. 一种多极化SAR舰船目标与方位向模糊鉴别方法[J]. 雷达学报, 2015, 4(1): 84–92. doi: 10.12000/JR14147.ZHOU Wei, SUN Yanli, XU Chengbin, et al. A method for discrimination of ship target and azimuth ambiguity in multi-polarimetric SAR imagery[J]. Journal of Radars, 2015, 4(1): 84–92. doi: 10.12000/JR14147. -
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- Figure 1. Optical images of bulk carriers, container ships and oil tankers
- Figure 2. Scattering mechanism of the vessels
- Figure 3. Flow chart of the proposed method
- Figure 4. Flow chart of the non-fixed threshold division method
- Figure 5. Initial classification plot of the non-fixed threshold division method
- Figure 6. Pauli pseudo-color image of the FP-SAR over Shanghai, China
- Figure 7. Results of the proposed method for bulk carriers
- Figure 8. Results of the proposed method for container ships
- Figure 9. Results of the proposed method for oil tankers
- Figure 10. Results of the four images for bulk carriers
- Figure 11. The scattering feature ratio corresponding to these four methods for bulk carriers
- Figure 12. Results of the four images for container ships
- Figure 13. The scattering feature ratio corresponding to these four methods for container ships
- Figure 14. Results of the four images for oil tankers
- Figure 15. The scattering feature ratio corresponding to these four methods for oil tankers
- Figure 16. Yamaguchi/H validation of validity
- Figure 17. GSP-Wishart validation of validity
- Figure 18. Comparison results between the multi-component decomposition methods and the proposed method of bulk carriers
- Figure 19. Comparison results between the multi-component decomposition methods and the proposed method of container ships
- Figure 20. Comparison results between the multi-component decomposition methods and the proposed method of oil tankers
- Figure 21. The average number of pixels for each type of scattering mechanism for the three types of ships as processed method