Polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency

LUO Jiahao; YIN Junjun; YANG Jian

doi:10.13374/j.issn2095-9389.2022.12.28.002

Article Contents

Article Navigation > Chinese Journal of Engineering > 2023 > Uncorrected proof

LUO Jiahao, YIN Junjun, YANG Jian. Polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2022.12.28.002

Citation:

LUO Jiahao, YIN Junjun, YANG Jian. Polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2022.12.28.002

Citation:

PDF( 3762 KB)

Polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency

doi: 10.13374/j.issn2095-9389.2022.12.28.002

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

More Information

Corresponding author: E-mail: junjun_yin@ustb.edu.cn
Received Date: 2022-12-28
Available Online: 2023-03-21

Abstract

Abstract

Polarimetric SAR ship detection is an important application of the polarimetric SAR system. Existing polarimetric SAR ship detection methods are plagued by erroneous detection of strong clutter and missed detection of small targets in multiscale situations. Particularly, the existing methods easily detect strong clutter as the target under strong background clutter, resulting in false alarms; in the case of multiscale ship detection, small ships are easily submerged in background clutter, resulting in missed detection of small targets. To solve these problems, this paper proposes a polarimetric SAR ship detection method based on superpixels and sparse reconstruction saliency. This method has two stages. In the first stage, the large polarimetric SAR ship detection scene image is segmented using the superpixel segmentation method to obtain a superpixel image. With the superpixel as the basic unit, a saliency detection method based on sparse reconstruction is used to obtain the saliency value of each superpixel in the image. Then, the superpixels that may contain ship targets are retained using the sea surface ship density defined in this paper. Accordingly, in the first stage, the superpixel regions that may contain ship targets are obtained through superpixel segmentation and sparse reconstruction saliency detection. Next, in the second stage, a saliency detection method based on sparse reconstruction is used to obtain the saliency value of each pixel in these reserved superpixel regions. Finally, the global threshold segmentation method is used for the pixels in these regions to obtain the final detection results of ship targets. In this paper, two polarimetric SAR images of the ALOS-2 satellite with different scenes were selected for an experiment. One image contains strong clutter on the sea surface; the other contains ships of different sizes and many small ships. The experimental results show that the proposed method can well determine the superpixel regions that may contain ship targets in the first stage and successfully obtain the ship detection results in the second stage. In addition, in both scenarios, the classic constant false alarm rate (CFAR) methods and a saliency detection method are used for comparison with the proposed method. The experimental results show that the proposed method produces almost no false alarms because it is insensitive to strong clutter; moreover, this method rarely misses small ship targets in the multiscale ship detection scene. The figure of merit of the proposed method reaches 94.87% in the strong clutter scene and 94.05% in the multiscale ship detection scene.
- polarimetric SAR,
- ship detection,
- sparse reconstruction,
- superpixel segmentation,
- saliency detection

FullText(HTML)

References(36)

References

[1]	王雪松, 陳思偉. 合成孔徑雷達極化成像解譯識別技術的進展與展望. 雷達學報, 2020, 9(2):259 doi: 10.12000/JR19109 Wang X S, Chen S W. Polarimetric synthetic aperture radar interpretation and recognition: Advances and perspectives. J Radars, 2020, 9(2): 259 doi: 10.12000/JR19109
[2]	杜蘭, 王兆成, 王燕, 等. 復雜場景下單通道SAR目標檢測及鑒別研究進展綜述. 雷達學報, 2020, 9(1):34 doi: 10.12000/JR19104 Du L, Wang Z C, Wang Y, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes. J Radars, 2020, 9(1): 34 doi: 10.12000/JR19104
[3]	鄧云凱, 禹衛東, 張衡, 等. 未來星載SAR技術發展趨勢. 雷達學報, 2020, 9(1):1 doi: 10.12000/JR20008 Deng Y K, Yu W D, Zhang H, et al. Forthcoming spaceborne SAR development. J Radars, 2020, 9(1): 1 doi: 10.12000/JR20008
[4]	張杰, 張晰, 范陳清, 等. 極化SAR在海洋探測中的應用與探討. 雷達學報, 2016, 5(6):596 Zhang J, Zhang X, Fan C Q, et al. Discussion on application of polarimetric synthetic aperture radar in marine surveillance. J Radars, 2016, 5(6): 596
[5]	王雪松. 雷達極化技術研究現狀與展望. 雷達學報, 2016, 5(2):119 doi: 10.12000/JR16039 Wang X S. Status and prospects of radar polarimetry techniques. J Radars, 2016, 5(2): 119 doi: 10.12000/JR16039
[6]	劉濤, 楊子淵, 蔣燕妮, 等. 極化SAR圖像艦船目標檢測研究綜述. 雷達學報, 2021, 10(1):1 doi: 10.12000/JR20155 Liu T, Yang Z Y, Jiang Y N, et al. Review of ship detection in polarimetric synthetic aperture imagery. J Radars, 2021, 10(1): 1 doi: 10.12000/JR20155
[7]	崔興超, 粟毅, 陳思偉. 融合極化旋轉域特征和超像素技術的極化SAR艦船檢測. 雷達學報, 2021, 10(1):35 doi: 10.12000/JR20147 Cui X C, Su Y, Chen S W. Polarimetric SAR ship detection based on polarimetric rotation domain features and superpixel technique. J Radars, 2021, 10(1): 35 doi: 10.12000/JR20147
[8]	Liu T, Yang Z Y, Yang J, et al. CFAR ship detection methods using compact polarimetric SAR in a K-Wishart distribution. IEEE J STARS, 2019, 12(10): 3737
[9]	Pappas O, Achim A, Bull D. Superpixel-level CFAR detectors for ship detection in SAR imagery. IEEE Geosci Remote Sens Lett, 2018, 15(9): 1397 doi: 10.1109/LGRS.2018.2838263
[10]	Gao G. A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images. IEEE Geosci Remote Sens Lett, 2011, 8(3): 557 doi: 10.1109/LGRS.2010.2090492
[11]	黃寅禮, 孫路, 郭亮, 等. 基于空間變跡濾波旁瓣抑制與有序統計恒虛警率的艦船檢測算法. 雷達學報, 2020, 9(2):335 doi: 10.12000/JR19082 Huang Y L, Sun L, Guo L, et al. Ship detection algorithm based on spatially variant apodization sidelobe suppression and order statistic-constant false alarm rate. J Radars, 2020, 9(2): 335 doi: 10.12000/JR19082
[12]	陳祥, 孫俊, 尹奎英, 等. 基于CFAR級聯的SAR圖像艦船目標檢測算法. 現代雷達, 2012, 34(9):50 doi: 10.3969/j.issn.1004-7859.2012.09.011 Chen X, Sun J, Yin K Y, et al. An algorithm of ship target detection in SAR images based on cascaded CFAR. Mod Radar, 2012, 34(9): 50 doi: 10.3969/j.issn.1004-7859.2012.09.011
[13]	An W T, Xie C H, Yuan X Z. An improved iterative censoring scheme for CFAR ship detection with SAR imagery. IEEE Trans Geosci Remote Sens, 2014, 52(8): 4585 doi: 10.1109/TGRS.2013.2282820
[14]	Weiss M. Analysis of some modified cell-averaging CFAR processors in multiple-target situations. IEEE Trans Aerosp Electron Syst, 1982, AES-18(1): 102 doi: 10.1109/TAES.1982.309210
[15]	Novak L M, Burl M C, Irving W W. Optimal polarimetric processing for enhanced target detection. IEEE Trans Aerosp Electron Syst, 1993, 29(1): 234 doi: 10.1109/7.249129
[16]	Goldstein G B. False-alarm regulation in log-normal and weibull clutter. IEEE Trans Aerosp Electron Syst, 1973, AES-9(1): 84 doi: 10.1109/TAES.1973.309705
[17]	Ritcey J A, Du H. Order statistic CFAR detectors for speckled area targets in SAR // Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers. Pacific Grove, 1991: 1082
[18]	Anastassopoulos V, Lampropoulos G A. Optimal CFAR detection in weibull clutter. IEEE Trans Aerosp Electron Syst, 1995, 31(1): 52 doi: 10.1109/7.366292
[19]	Leng X G, Ji K F, Xing X W, et al. Area ratio invariant feature group for ship detection in SAR imagery. IEEE J Sel Top Appl Earth Obs Remote Sens, 2018, 11(7): 2376 doi: 10.1109/JSTARS.2018.2820078
[20]	Fan W W, Zhou F, Bai X R, et al. Ship detection using deep convolutional neural networks for PolSAR images. Remote Sens, 2019, 11(23): 2862 doi: 10.3390/rs11232862
[21]	Chang Y L, Anagaw A, Chang L N, et al. Ship detection based on YOLOv2 for SAR imagery. Remote Sens, 2019, 11(7): 786 doi: 10.3390/rs11070786
[22]	張曉玲, 張天文, 師君, 等. 基于深度分離卷積神經網絡的高速高精度SAR艦船檢測. 雷達學報, 2019, 8(6):841 doi: 10.12000/JR19111 Zhang X L, Zhang T W, Shi J, et al. High-speed and high-accurate SAR ship detection based on a depthwise separable convolution neural network. J Radars, 2019, 8(6): 841 doi: 10.12000/JR19111
[23]	田壯壯, 占榮輝, 胡杰民, 等. 基于卷積神經網絡的SAR圖像目標識別研究. 雷達學報, 2016, 5(3):320 doi: 10.12000/JR16037 Tian Z Z, Zhan R H, Hu J M, et al. SAR ATR based on convolutional neural network. J Radars, 2016, 5(3): 320 doi: 10.12000/JR16037
[24]	Chen S W, Tao C S, Wang X S, et al. Polarimetric SAR targets detection and classification with deep convolutional neural network // 2018 Progress in Electromagnetics Research Symposium (PIERS-Toyama). Toyama, 2019: 2227
[25]	Zhou F, Fan W W, Sheng Q Q, et al. Ship detection based on deep convolutional neural networks for polsar images // IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, 2018: 681
[26]	Jin K, Chen Y L, Xu B, et al. A patch-to-pixel convolutional neural network for small ship detection with PolSAR images. IEEE Trans Geosci Remote Sens, 2020, 58(9): 6623 doi: 10.1109/TGRS.2020.2978268
[27]	Zhai L, Li Y, Su Y. Inshore ship detection via saliency and context information in high-resolution SAR images. IEEE Geosci Remote Sens Lett, 2016, 13(12): 1870 doi: 10.1109/LGRS.2016.2616187
[28]	Cui X C, Su Y, Chen S W. A saliency detector for polarimetric SAR ship detection using similarity test. IEEE J Sel Top Appl Earth Obs Remote Sens, 2019, 12(9): 3423 doi: 10.1109/JSTARS.2019.2925833
[29]	Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell, 2009, 31(2): 210 doi: 10.1109/TPAMI.2008.79
[30]	Li X H, Lu H C, Zhang L H, et al. Saliency detection via dense and sparse reconstruction // 2013 IEEE International Conference on Computer Vision. Sydney, 2013: 2976
[31]	Lu H C, Li X H, Zhang L H, et al. Dense and sparse reconstruction error based saliency descriptor. IEEE Trans Image Process, 2016, 25(4): 1592 doi: 10.1109/TIP.2016.2524198
[32]	Borji A, Itti L. Exploiting local and global patch rarities for saliency detection // 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, 2012: 478
[33]	Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell, 2012, 34(11): 2274 doi: 10.1109/TPAMI.2012.120
[34]	Qin F C, Guo J M, Lang F K. Superpixel segmentation for polarimetric SAR imagery using local iterative clustering. IEEE Geosci Remote Sens Lett, 2015, 12(1): 13 doi: 10.1109/LGRS.2014.2322960
[35]	Li T, Liu Z, Xie R, et al. An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images. IEEE J Sel Top Appl Earth Obs Remote Sens, 2018, 11(1): 184 doi: 10.1109/JSTARS.2017.2764506
[36]	Cui X C, Tao C S, Su Y, et al. PolSAR ship detection based on polarimetric correlation pattern. IEEE Geosci Remote Sens Lett, 2021, 18(3): 471 doi: 10.1109/LGRS.2020.2976477