一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

张帆; 陆圣涛; 项德良; 袁新哲

doi:10.12000/JR22067

一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

DOI: 10.12000/JR22067

张帆¹,
陆圣涛¹,
项德良^{1, 2, ,},
袁新哲³

1.
北京化工大学信息科学与技术学院北京 100029
2.
北京化工大学软物质科学与工程高精尖创新中心北京 100029
3.
国家卫星海洋应用中心北京 100081

基金项目: 国家自然科学基金(41976174)

详细信息

作者简介:
张　帆，教授，博士生导师，主要研究方向为SAR信号处理、SAR图像解译、高性能计算等

陆圣涛，硕士生，主要研究方向为人工智能、雷达图像处理

项德良，教授，主要研究方向为SAR/PolSAR信息处理、探地雷达等

袁新哲，副研究员，主要研究方向为SAR海洋遥感

通讯作者:
项德良 xiangdeliang@gmail.com

责任主编：张晓玲 Corresponding Editor: ZHANG Xiaoling
中图分类号: TN959.72
计量
- 文章访问数: 1993
- HTML全文浏览量: 1312
- PDF下载量: 386
- 被引次数: 9
出版历程
- 收稿日期: 2022-04-09
- 修回日期: 2022-05-16
- 网络出版日期: 2022-05-29
- 刊出日期: 2023-02-28

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

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

摘要

摘要: 合成孔径雷达(SAR)图像舰船目标检测一直受到学者广泛关注，恒虚警率(CFAR)检测算法作为雷达图像经典目标检测算法被广泛应用于SAR图像舰船目标检测中。然而经典CFAR检测性能容易受到相干斑噪声影响，基于滑窗的检测结果对滑窗的尺寸选择非常敏感，难以保证杂波背景中不存在目标像素，并且计算效率较低。针对上述问题，该文提出了一种新的基于超像素无窗快速CFAR的SAR图像舰船目标检测算法。首先，利用基于密度的快速噪声空间聚类(DBSCAN)超像素生成方法生成SAR图像的超像素。在SAR数据服从混合瑞利分布的假设下，定义了超像素相异度。然后利用超像素精确估计每个像素的杂波参数，即使在多目标情况下，也可以克服传统CFAR滑动窗口的缺点。此外，基于SAR图像变异系数，提出了一种基于变异系数的局部超像素对比度来优化CFAR检测，以此消除大量杂波虚警，如陆地区域人造目标。对5幅SAR图像的实验结果表明，与其他方法相比，该文方法对不同场景SAR图像海面舰船目标检测都十分稳健。
- 合成孔径雷达(SAR) /
- 恒虚警率(CFAR) /
- 变异系数 /
- 超像素 /
- 目标检测
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.
- Synthetic Aperture Radar (SAR) /
- Constant False Alarm Rate (CFAR) /
- Coefficient of Variation (CoV) /
- Superpixel /
- Target detection

HTML全文

图 1 传统CFAR检测算法通用流程

Figure 1. General flow of the traditional CFAR detection algorithm

下载: 全尺寸图片幻灯片

图 2 传统CFAR检测器滑动窗口示意图

Figure 2. Schematic diagram of the sliding window in the traditional CFAR detector

下载: 全尺寸图片幻灯片

图 3 本文提出的基于超像素无窗快速CFAR的目标检测算法流程

Figure 3. Flow chart the proposed superpixel non-window fast CFAR strategy

下载: 全尺寸图片幻灯片

图 4 杂波超像素选取策略

Figure 4. Strategies for clutter superpixels determination

下载: 全尺寸图片幻灯片

图 5 超像素相异度有效性分析示意图

Figure 5. Schematic diagram of superpixel dissimilarity effectiveness analysis

下载: 全尺寸图片幻灯片

图 6 实验采用的不同波段不同分辨率的SAR图像

Figure 6. The SAR images with different bands and different resolutions in the experiment

下载: 全尺寸图片幻灯片

图 7 5幅SAR图像的真值图

Figure 7. The ground truth map of the SAR images

下载: 全尺寸图片幻灯片

图 8 TerraSAR X波段SAR图像1检测结果图

Figure 8. The results of TerraSAR X band SAR image 1 with the proposed method

下载: 全尺寸图片幻灯片

图 9 TerraSAR X波段SAR图像2检测结果图

Figure 9. The results of TerraSAR X band SAR image 2 with the proposed method

下载: 全尺寸图片幻灯片

图 10 GF-3 C波段SAR图像1检测结果图

Figure 10. The results of GF-3 C band SAR image 1 with the proposed method

下载: 全尺寸图片幻灯片

图 11 GF-3 C波段SAR图像2检测结果图

Figure 11. The results of GF-3 C band SAR image 2 with the proposed method

下载: 全尺寸图片幻灯片

图 12 Sentinel-1A C波段SAR图像检测结果图

Figure 12. The results of Sentinel-1A C band SAR image with the proposed method

下载: 全尺寸图片幻灯片

图 13 3种对比方法对TerraSAR X波段SAR图像1的检测结果

Figure 13. The detection results of TerraSAR X band SAR image 1 with three compared methods

下载: 全尺寸图片幻灯片

图 14 3种对比方法对TerraSAR X波段SAR图像2的检测结果

Figure 14. The detection results of TerraSAR X band SAR image 2 with three compared methods

下载: 全尺寸图片幻灯片

图 15 3种对比方法对GF-3 C波段SAR图像1的检测结果

Figure 15. The detection results of GF-3 C band SAR image 1 with three compared methods

下载: 全尺寸图片幻灯片

图 16 3种对比方法对GF-3 C波段SAR图像2的检测结果

Figure 16. The detection results of GF-3 C band SAR image 2 with three compared methods

下载: 全尺寸图片幻灯片

图 17 3种对比方法对Sentinel-1A C波段SAR图像的检测结果

Figure 17. The detection results of Sentinel-1A C band SAR image with three compared methods

下载: 全尺寸图片幻灯片

图 18 不同方法对TerraSAR X波段SAR图像1舰船检测的ROC曲线

Figure 18. The ROC curve of ship detection in TerraSAR X band SAR image 1 by different methods

下载: 全尺寸图片幻灯片

图 19 概率密度拟合性能测试结果图

Figure 19. Test results of probability density fitting performance

下载: 全尺寸图片幻灯片

表 1 杂波超像素选取算法

Table 1. The algorithm of clutter superpixels selection

输入：SAR图像全体超像素集R_all，相异性阈值Th_clu, Tl_clu和S_clutter中超像素数量最大值
输出：每个潜在目标超像素的S_clutter
算法步骤：
(1) 根据每个超像素的平均强度值采用K-means聚类算法将R_all分成两个子集R_target和R_background
(2) for R_target中每一个潜在目标超像素SP_i，do
(3)　　 for SP_i的每一个邻接超像素 ${\rm{SP}}_i^k$ , ${\rm{SP}}_i^k$ ∈R_background， do
(4) 　　　　计算超像素相异度 Ω(SP_i, ${\rm{SP}}_i^k$ )
(5) 　　相异度值大于Th_clu的超像素 ${\rm{SP}}_i^k$ 被添加进杂波超像素集S_clutter
(6) 　　 end
(7) 　　 for S_clutter杂波超像素集中超像素 $S_{{\text{clutter}}}^i$ 的每一个邻接超像素，do
(8) 　　　　计算 $S_{{\text{clutter}}}^i$ 与每一个邻接超像素的相异度
(9) 　　相异度值小于Tl_clu的邻接超像素被添加进杂波超像素集S_clutter
(10) 　　end
(11) 　　重复执行步骤(7)—步骤(10)直到满足S_clutter中超像素数量达到预设的最大值
(12) end

下载: 导出CSV

表 2 4种目标检测算法性能比较

Table 2. Quantitative comparisons of four target detection algorithms

SAR图像	双参数CFAR		SP-CFAR		SP-CFAR-TG		本文方法
SAR图像	FPR(%)	TPR(%)	FPR(%)	TPR(%)	FPR(%)	TPR(%)	FPR(%)	TPR(%)
TerraSAR X波段SAR图像1	3.19	83.41	2.95	23.64	0.54	82.85	0.31	87.93
TerraSAR X波段SAR图像2	2.26	77.23	2.59	69.62	0.98	75.41	0.27	85.57
GF-3 C波段SAR图像1	4.23	99.50	3.76	99.21	0.89	94.95	0.02	96.19
GF-3 C波段SAR图像2	4.61	99.44	0.26	96.25	0.79	95.95	0.02	98.49
Sentinel-1A C波段SAR图像	2.77	98.64	0.85	85.67	0.61	92.48	0.19	97.36

下载: 导出CSV

表 3 本文方法对5幅SAR图像阴影超像素去除前后的检测性能比较

Table 3. Quantitative measures of the proposed method for five SAR images with and without shadow superpixels removal

SAR图像	进行阴影超像素去除		不进行阴影超像素去除
SAR图像	FPR (%)	TPR (%)	FPR (%)	TPR (%)
TerraSAR X波段SAR图像1	0.31	87.93	0.32	86.98
TerraSAR X波段SAR图像2	0.27	85.57	0.27	85.11
GF-3 C波段SAR图像1	0.02	96.19	0.03	95.62
GF-3 C波段SAR图像2	0.02	98.49	0.02	98.25
Sentinel-1A C波段SAR图像	0.19	97.36	0.19	97.36

下载: 导出CSV

表 4 本文方法在不同超像素数量情况下对TerraSARX波段SAR图像1检测性能比较

Table 4. Quantitative measures of the proposed method for TerraSAR X band SAR image 1 with different superpixel numbers

超像素数量	FPR(%)	TPR(%)
3000	0.33	84.36
4000	0.36	86.81
5000	0.31	87.93
6000	0.37	87.88

下载: 导出CSV

表 5 4种目标检测算法时间比较

Table 5. Time costs of four ship detection algorithms

SAR图像	双参数CFAR(s)	SP-CFAR(s)	SP-CFAR-TG(s)	本文方法(s)
TerraSAR X波段SAR图像1	113.41	41.42	18.77	10.86
TerraSAR X波段SAR图像2	1379.21	498.56	86.13	31.75
GF-3 C波段SAR图像1	1428.17	461.73	77.31	29.82
GF-3 C波段SAR图像2	351.12	98.73	43.36	22.93
Sentinel-1A C波段SAR图像	158.35	56.19	30.51	14.31

下载: 导出CSV

表 6 本文方法在不同超像素数量情况下对TerraSAR X波段SAR图像1检测时间比较

Table 6. Time costs of the proposed method for TerraSAR X band SAR image 1 with different superpixel numbers

超像素数量	时间(s)
3000	8.63
4000	9.15
5000	10.86
6000	14.38

下载: 导出CSV

参考文献(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

施引文献

期刊类型引用(5)

1.	布锦钶，李鹏飞，周鹏，周志一，曾祥祝，胡城志，孙兴赛，赵青，张文理. 基于杂波知识图谱驱动的空时自适应处理方法. 物联网技术. 2024(11): 122-126 . 百度学术
2.	马晖，胡敦法，师竹雨，刘宏伟. 基于涡旋电磁波的雷达应用研究进展. 现代雷达. 2023(05): 27-41 . 百度学术
3.	印必还，何姿，丁大志. 基于旋转多普勒效应的自旋目标转速估计方法. 物理学报. 2023(17): 71-83 . 百度学术
4.	罗迎，袁航，袁延鑫. 单频涡旋电磁波雷达旋转目标微动参数提取方法. 信号处理. 2023(09): 1587-1595 . 百度学术
5.	张飞飞. 基于槽波和CT成像的矿区煤系地质复杂工作面隐伏构造精细化探测. 能源与环保. 2023(10): 167-171 . 百度学术