Volume 12 Issue 1
Feb.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2023  >  12(1): 120-139
ZHANG Fan, LU Shengtao, XIANG Deliang, et al. An improved superpixel-based CFAR method for high-resolution SAR image ship target detection[J]. Journal of Radars, 2023, 12(1): 120–139. doi: 10.12000/JR22067
Citation: ZHANG Fan, LU Shengtao, XIANG Deliang, et al. An improved superpixel-based CFAR method for high-resolution SAR image ship target detection[J]. Journal of Radars, 2023, 12(1): 120–139. doi: 10.12000/JR22067
shu

An Improved Superpixel-based CFAR Method for High-resolution SAR Image Ship Target Detection

doi: 10.12000/JR22067
  • 1.

    College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

  • 2.

    Beijing Advanced Innovation Center for Soft Matter Science and Engineering, Beijing University of Chemical Technology, Beijing 100029, China

  • 3.

    National Satellite Ocean Application Service, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (41976174)
More Information
  • Corresponding author: XIANG Deliang, xiangdeliang@gmail.com
  • Received Date: 2022-04-09
  • Accepted Date: 2022-05-12
  • Rev Recd Date: 2022-05-16
    • Available Online: 2022-05-23
  • Publish Date: 2022-05-29
    Abstract
  • Synthetic Aperture Radar (SAR) image ship target detection has attracted considerable attention. As a state-of-the-art method, the Constant False Alarm Rate (CFAR) detection algorithm is often used in SAR image ship target detection. However, the detection performance of the classical CFAR is easily affected by speckle noise. Moreover, the detection results based on the sliding window are sensitive to the size of the sliding window. Thus, ensuring that there are no target pixels in the cluttered background is difficult, which easily leads to a high computational load. This study proposes a new ship target detection method for SAR images based on fast superpixel-based non-window CFAR to solve these problems. The superpixel generation method of Density Based Spatial Clustering of Applications with Noise is used to generate superpixels for SAR images. Under the assumption that SAR data obey the Rayleigh mixture distribution, we define a superpixel dissimilarity measure. Then, the clutter parameters of each pixel are accurately estimated using superpixels, which can avoid the shortcomings of the traditional CFAR sliding window even in the case of multiple targets. A local contrast based on the Coefficient of Variation (CoV) of the SAR image is proposed to optimize the CFAR detection result, which can eliminate a large number of false alarms from man-made targets in urban areas. The experimental results of five real SAR images show that the proposed method for ship target detection in SAR images with different scenes is robust compared with other state-of-the-art methods.

     

    • FullText(HTML)
  • loading
    • References(42)
  • [1]
    杜兰, 王兆成, 王燕, 等. 复杂场景下单通道SAR目标检测及鉴别研究进展综述[J]. 雷达学报, 2020, 9(1): 34–54. doi: 10.12000/JR19104

    DU Lan, WANG Zhaocheng, WANG Yan, et al. Survey of research progress on target detection and discrimination of single-channel SAR images for complex scenes[J]. Journal of Radars, 2020, 9(1): 34–54. doi: 10.12000/JR19104
    [2]
    马俊虎, 刘长远, 甘露. 基于压缩感知的CFAR目标检测算法[J]. 电子与信息学报, 2017, 39(12): 2899–2904. doi: 10.11999/JEIT170382

    MA Junhu, LIU Changyuan, and GAN Lu. CFAR target detection algorithm based on compressive sensing[J]. Journal of Electronics &Information Technology, 2017, 39(12): 2899–2904. doi: 10.11999/JEIT170382
    [3]
    许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084

    XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084
    [4]
    李春升, 于泽, 陈杰. 高分辨率星载SAR成像与图像质量提升方法综述[J]. 雷达学报, 2019, 8(6): 717–731. doi: 10.12000/JR19085

    LI Chunsheng, YU Ze, and CHEN Jie. Overview of techniques for improving high-resolution spaceborne SAR imaging and image quality[J]. Journal of Radars, 2019, 8(6): 717–731. doi: 10.12000/JR19085
    [5]
    房明星, 毕大平, 沈爱国, 等. 对SAR图像恒虚警检测的多假目标干扰研究[J]. 电子与信息学报, 2017, 39(4): 973–980. doi: 10.11999/JEIT160633

    FANG Mingxing, BI Daping, SHEN Aiguo, et al. Jamming technique of multiple false targets against CFAR detection in SAR images[J]. Journal of Electronics &Information Technology, 2017, 39(4): 973–980. doi: 10.11999/JEIT160633
    [6]
    黄寅礼, 孙路, 郭亮, 等. 基于空间变迹滤波旁瓣抑制与有序统计恒虚警率的舰船检测算法[J]. 雷达学报, 2020, 9(2): 335–342. doi: 10.12000/JR19082

    HUANG Yinli, SUN Lu, GUO Liang, et al. Ship detection algorithm based on spatially variant apodization sidelobe suppression and order statistic-constant false alarm rate[J]. Journal of Radars, 2020, 9(2): 335–342. doi: 10.12000/JR19082
    [7]
    SCHWEGMANN C P, KLEYNHANS W, and SALMON B P. Manifold adaptation for constant false alarm rate ship detection in south african oceans[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3329–3337. doi: 10.1109/JSTARS.2015.2417756
    [8]
    QIN Xianxiang, ZHOU Shilin, ZOU Huanxin, et al. A CFAR detection algorithm for generalized Gamma distributed background in high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 806–810. doi: 10.1109/LGRS.2012.2224317
    [9]
    朱洁丽, 汤俊. 基于改进的ZMNL和SIRP的K分布杂波模拟方法[J]. 雷达学报, 2014, 3(5): 533–540. doi: 10.3724/SP.J.1300.2014.13124

    ZHU Jieli and TANG Jun. K-distribution clutter simulation methods based on improved ZMNL and SIRP[J]. Journal of Radars, 2014, 3(5): 533–540. doi: 10.3724/SP.J.1300.2014.13124
    [10]
    SHAN Zili, WANG Chao, ZHANG Hong, et al. Change detection in urban areas with high resolution SAR images using second kind statistics based G0 distribution[C]. 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, USA, 2010: 4600–4603.
    [11]
    孙显, 王智睿, 孙元睿, 等. AIR-SARShip-1.0: 高分辨率SAR舰船检测数据集[J]. 雷达学报, 2019, 8(6): 852–862. doi: 10.12000/JR19097

    SUN Xian, WANG Zhirui, SUN Yuanrui, et al. AIR-SARShip-1.0: High-resolution SAR ship detection dataset[J]. Journal of Radars, 2019, 8(6): 852–862. doi: 10.12000/JR19097
    [12]
    LI Dong, LIANG Quanhuan, LIU Hongqing, et al. A novel multidimensional domain deep learning network for SAR ship detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5203213. doi: 10.1109/TGRS.2021.3062038
    [13]
    张晓玲, 张天文, 师君, 等. 基于深度分离卷积神经网络的高速高精度SAR舰船检测[J]. 雷达学报, 2019, 8(6): 841–851. doi: 10.12000/JR19111

    ZHANG Xiaoling, ZHANG Tianwen, SHI Jun, et al. High-speed and high-accurate SAR ship detection based on a depthwise separable convolution neural network[J]. Journal of Radars, 2019, 8(6): 841–851. doi: 10.12000/JR19111
    [14]
    WU Zitong, HOU Biao, REN Bo, et al. A deep detection network based on interaction of instance segmentation and object detection for SAR images[J]. Remote Sensing, 2021, 13(13): 2582. doi: 10.3390/rs13132582
    [15]
    SUN Yuanrui, SUN Xian, WANG Zhirui, et al. Oriented ship detection based on strong scattering points network in large-scale SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5218018. doi: 10.1109/TGRS.2021.3130117
    [16]
    CUI Yi, ZHOU Guangyi, YANG Jian, et al. On the iterative censoring for target detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 641–645. doi: 10.1109/LGRS.2010.2098434
    [17]
    AN Wentao, XIE Chunhua, and YUAN Xinzhe. An improved iterative censoring scheme for CFAR ship detection with SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4585–4595. doi: 10.1109/TGRS.2013.2282820
    [18]
    GAO Gui, LIU Li, ZHAO Lingjun, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685–1697. doi: 10.1109/TGRS.2008.2006504
    [19]
    HOU Biao, CHEN Xingzhong, and JIAO Licheng. Multilayer CFAR detection of ship targets in very high resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 811–815. doi: 10.1109/LGRS.2014.2362955
    [20]
    LENG Xiangguang, JI Kefeng, YANG Kai, et al. A bilateral CFAR algorithm for ship detection in SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1536–1540. doi: 10.1109/LGRS.2015.2412174
    [21]
    艾加秋, 曹振翔, 毛宇翔, 等. 一种复杂环境下改进的SAR图像双边CFAR舰船检测算法[J]. 雷达学报, 2021, 10(4): 499–515. doi: 10.12000/JR20127

    AI Jiaqiu, CAO Zhenxiang, MAO Yuxiang, et al. An improved bilateral CFAR ship detection algorithm for SAR image in complex environment[J]. Journal of Radars, 2021, 10(4): 499–515. doi: 10.12000/JR20127
    [22]
    WANG Zhaocheng, DU Lan, and SU Hongtao. Target detection via Bayesian-morphological saliency in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5455–5466. doi: 10.1109/TGRS.2017.2707672
    [23]
    JIA Sen, DENG Xianglong, XU Meng, et al. Superpixel-level weighted label propagation for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 5077–5091. doi: 10.1109/TGRS.2020.2972294
    [24]
    HE Jinglu, WANG Yinghua, LIU Hongwei, et al. A novel automatic PolSAR ship detection method based on superpixel-level local information measurement[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(3): 384–388. doi: 10.1109/LGRS.2017.2789204
    [25]
    崔兴超, 粟毅, 陈思伟. 融合极化旋转域特征和超像素技术的极化SAR舰船检测[J]. 雷达学报, 2021, 10(1): 35–48. doi: 10.12000/JR20147

    CUI Xingchao, SU Yi, and CHEN Siwei. Polarimetric SAR ship detection based on polarimetric rotation domain features and superpixel technique[J]. Journal of Radars, 2021, 10(1): 35–48. doi: 10.12000/JR20147
    [26]
    聂茜茜, 肖斌, 毕秀丽, 等. 基于超像素级卷积神经网络的多聚焦图像融合算法[J]. 电子与信息学报, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053

    NIE Xixi, XIAO Bin, BI Xiuli, et al. Multi-focus image fusion algorithm based on super pixel level convolutional neural network[J]. Journal of Electronics &Information Technology, 2021, 43(4): 965–973. doi: 10.11999/JEIT191053
    [27]
    ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274–2282. doi: 10.1109/TPAMI.2012.120
    [28]
    XIANG Deliang, TANG Tao, ZHAO Lingjun, et al. Superpixel generating algorithm based on pixel intensity and location similarity for SAR image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(6): 1414–1418. doi: 10.1109/LGRS.2013.2259214
    [29]
    JING Wenbo, JIN Tian, and XIANG Deliang. Content-sensitive superpixel generation for SAR images with edge penalty and contraction-expansion search strategy[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5210715. doi: 10.1109/TGRS.2021.3077407
    [30]
    JING Wenbo, JIN Tian, and XIANG Deliang. Edge-aware superpixel generation for SAR imagery with one iteration merging[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(9): 1600–1604. doi: 10.1109/LGRS.2020.3005973
    [31]
    XIANG Deliang, TANG Tao, QUAN Sinong, et al. Adaptive superpixel generation for SAR images with linear feature clustering and edge constraint[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(6): 3873–3889. doi: 10.1109/TGRS.2018.2888891
    [32]
    CUI Zongyong, HOU Zesheng, YANG Hongzhi, et al. A CFAR target-detection method based on superpixel statistical modeling[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(9): 1605–1609. doi: 10.1109/LGRS.2020.3006033
    [33]
    YU Wenyi, WANG Yinghua, LIU Hongwei, et al. Superpixel-based CFAR target detection for high-resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 730–734. doi: 10.1109/LGRS.2016.2540809
    [34]
    PAPPAS O, ACHIM A, and BULL D. Superpixel-level CFAR detectors for ship detection in SAR imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(9): 1397–1401. doi: 10.1109/LGRS.2018.2838263
    [35]
    LI Tao, LIU Zheng, XIE Rong, et al. An improved superpixel-level CFAR detection method for ship targets in high-resolution SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1): 184–194. doi: 10.1109/JSTARS.2017.2764506
    [36]
    LIU Ming, CHEN Shichao, LU Fugang, et al. Realizing target detection in SAR images based on multiscale superpixel fusion[J]. Sensors, 2021, 21(5): 1643. doi: 10.3390/s21051643
    [37]
    LI Mingdian, CUI Xingchao, and CHEN Siwei. Adaptive superpixel-level CFAR detector for SAR inshore dense ship detection[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4010405. doi: 10.1109/LGRS.2021.3059253
    [38]
    LI Tao, PENG Dongliang, CHEN Zhikun, et al. Superpixel-level CFAR detector based on truncated gamma distribution for SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18(8): 1421–1425. doi: 10.1109/LGRS.2020.3003659
    [39]
    ZHANG Liang, LU Shengtao, HU Canbin, et al. Superpixel generation for SAR imagery based on fast DBSCAN clustering with edge penalty[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 804–819. doi: 10.1109/JSTARS.2021.3131187
    [40]
    KURUOGLU E E and ZERUBIA J. Modelling SAR images with a generalisation of the Rayleigh distribution[C]. Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2000: 224–228.
    [41]
    LI Hengchao, KRYLOV V A, FAN Pingzhi, et al. Unsupervised learning of generalized gamma mixture model with application in statistical modeling of high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(4): 2153–2170. doi: 10.1109/TGRS.2015.2496348
    [42]
    NAR F, OKMAN O E, ÖZGÜR A, et al. Fast target detection in radar images using Rayleigh mixtures and summed area tables[J]. Digital Signal Processing, 2018, 77: 86–101. doi: 10.1016/j.dsp.2017.09.015
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(1637) PDF downloads(253) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return