Research on High-resolution 3D Imaging and Point Cloud Clustering of Array SAR

JI Ang; PEI Hao; ZHANG Bangjie; XU Gang

doi:10.11999/JEIT231223

Volume 46 Issue 5

May 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(5): 2087-2094

JI Ang, PEI Hao, ZHANG Bangjie, XU Gang. Research on High-resolution 3D Imaging and Point Cloud Clustering of Array SAR[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2087-2094. doi: 10.11999/JEIT231223

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JI Ang, PEI Hao, ZHANG Bangjie, XU Gang. Research on High-resolution 3D Imaging and Point Cloud Clustering of Array SAR[J]. Journal of Electronics & Information Technology, 2024, 46(5): 2087-2094. doi: 10.11999/JEIT231223

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Research on High-resolution 3D Imaging and Point Cloud Clustering of Array SAR

doi: 10.11999/JEIT231223

State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China

Funds: The National Natural Science Foundation of China (62071113), The Natural Science Foundation of Jiangsu Province (BK20211559)

Received Date: 2023-11-03
Rev Recd Date: 2024-02-10

Available Online: 2024-03-04

Publish Date: 2024-05-30

Abstract

Abstract

Compared with traditional Two-Dimensional (2D) Synthetic Aperture Radar (SAR) imaging, Three-Dimensional (3D) SAR imaging technology can overcome problems such as overlay and geometric distortion, thus having broad development space. As a typical 3D imaging system, the elevation resolution of array SAR is generally limited by the array aperture in theory, which is much lower than the range and azimuth resolution. To address this issue, an assumption of consistency in elevation between neighboring pixels is introduced and a re-weighted locally joint sparsity based Compressed Sensing (CS) approach is proposed for the array super-resolution imaging in the height dimension. Then, typical clustering methods such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to achieve clustering analysis of specific targets (such as buildings and vehicles) in the observation scene. Finally, the experimental analysis using measured data is performed to confirm the effectiveness of the proposed algorithm.
- Array SAR,
- Improved array resolution,
- 3D target clustering

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References(21)

References

[1]	ZHANG Bangjie, XU Gang, ZHOU Rui, et al. Multi-channel back-projection algorithm for mmWave automotive MIMO SAR imaging with Doppler-division multiplexing[J]. IEEE Journal of Selected Topics in Signal Processing, 2023, 17(2): 445–457. doi: 10.1109/JSTSP.2022.3207902.
[2]	ZHANG Bangjie, XU Gang, YU Hanwen, et al. Array 3-D SAR tomography using robust gridless compressed sensing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5205013. doi: 10.1109/TGRS.2023.3259980.
[3]	任烨仙, 徐丰. 若干层析SAR成像方法在解叠掩性能上的对比分析[J]. 雷达学报, 2022, 11(1): 71–82. doi: 10.12000/JR21139. REN Yexian and XU Feng. Comparative experiments on separation performance of overlapping scatterers with several tomography imaging methods[J]. Journal of Radars, 2022, 11(1): 71–82. doi: 10.12000/JR21139.
[4]	周良将, 汪丙南, 王亚超, 等. 机载多维度SAR航空观测系统实验初步进展[J]. 电子与信息学报, 2023, 45(4): 1243–1253. doi: 10.11999/JEIT220250. ZHOU Liangjiang, WANG Bingnan, WANG Yachao, et al. Preliminary process of airborne multidimensional space joint-observation SAR system[J]. Journal of Electronics & Information Technology, 2023, 45(4): 1243–1253. doi: 10.11999/JEIT220250.
[5]	仇晓兰, 焦泽坤, 彭凌霄, 等. SARMV3D-1.0: SAR微波视觉三维成像数据集[J]. 雷达学报, 2021, 10(4): 485–498. doi: 10.12000/JR21112. QIU Xiaolan, JIAO Zekun, PENG Lingxiao, et al. SARMV3D-1.0: Synthetic aperture radar microwave vision 3D imaging dataset[J]. Journal of Radars, 2021, 10(4): 485–498. doi: 10.12000/JR21112.
[6]	ZHU Xiaoxiang and BAMLER R. Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(1): 247–258. doi: 10.1109/TGRS.2011.2160183.
[7]	REN Yexian, XIAO Aoran, HU Fengming, et al. Coprime sensing for airborne array interferometric SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5229615. doi: 10.1109/TGRS.2022.3182980.
[8]	ZHU Xiaoxiang, GE Nan, and SHAHZAD M. Joint sparsity in SAR tomography for urban mapping[J]. IEEE Journal of Selected Topics in Signal Processing, 2015, 9(8): 1498–1509. doi: 10.1109/JSTSP.2015.2469646.
[9]	SHI Yilei, ZHU Xiaoxiang, and BAMLER R. Nonlocal compressive sensing-based SAR tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(5): 3015–3024. doi: 10.1109/TGRS.2018.2879382.
[10]	JIAO Zekun, DING Chibiao, QIU Xiaolan, et al. Urban 3D imaging using airborne TomoSAR: Contextual information-based approach in the statistical way[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 127–141. doi: 10.1016/j.isprsjprs.2020.10.013.
[11]	李杭, 梁兴东, 张福博, 等. 基于高斯混合聚类的阵列干涉SAR三维成像[J]. 雷达学报, 2017, 6(6): 630–639. doi: 10.12000/JR17020. LI Hang, LIANG Xingdong, ZHANG Fubo, et al. 3D imaging for array InSAR based on Gaussian mixture model clustering[J]. Journal of Radars, 2017, 6(6): 630–639. doi: 10.12000/JR17020.
[12]	ERTIN E, AUSTIN C D, SHARMA S, et al. GOTCHA experience report: Three-dimensional SAR imaging with complete circular apertures[C]. SPIE 6568, Algorithms for Synthetic Aperture Radar Imagery XIV, Orlando, USA, 2007: 656802. doi: 10.1117/12.723245.
[13]	ZHU Xiaoxiang and SHAHZAD M. Facade reconstruction using Multiview spaceborne TomoSAR point clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(6): 3541–3552. doi: 10.1109/TGRS.2013.2273619.
[14]	SHAHZAD M and ZHU Xiaoxiang. Automatic detection and reconstruction of 2-D/3-D building shapes from spaceborne TomoSAR point clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1292–1310. doi: 10.1109/TGRS.2015.2477429.
[15]	ZHANG Wei, WANG Ping, HE Ningyu, et al. Super resolution DOA based on relative motion for FMCW automotive radar[J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 8698–8709. doi: 10.1109/TVT.2020.2999640.
[16]	DANIEL J W. The conjugate gradient method for linear and nonlinear operator equations[J]. SIAM Journal on Numerical Analysis, 1967, 4(1): 10–26. doi: 10.1137/0704002.
[17]	LIU Zhiheng, ZHANG Wenjie, YU Hang, et al. Improved YOLOv5s for small ship detection with optical remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 8002205. doi: 10.1109/LGRS.2023.3319025.
[18]	LIU Wenxuan, JIN Taoyong, LI Jiancheng, et al. Adaptive clustering-based method for ICESat-2 sea ice retrieval[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4301814. doi: 10.1109/TGRS.2023.3287909.
[19]	ROUSSEEUW P J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis[J]. Journal of Computational and Applied Mathematics, 1987, 20: 53–65. doi: 10.1016/0377-0427(87)90125-7.
[20]	CALIŃSKI T and HARABASZ J. A dendrite method for cluster analysis[J]. Communications in Statistics, 1974, 3(1): 1–27. doi: 10.1080/03610927408827101.
[21]	LIU Fan and DENG Yong. Determine the number of unknown targets in open world based on elbow method[J]. IEEE Transactions on Fuzzy Systems, 2021, 29(5): 986–995. doi: 10.1109/TFUZZ.2020.2966182.