Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (6): 1904-1909.doi: 10.13229/j.cnki.jdxbgxb20170984

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Target detecting with conjugate CFAR in VHR SAR image

HUANG Yong1,2,3(),YANG De-yun2,QIAO Sai2,MU Zhen-guo3   

  1. 1. School of Computer Science and Technology, Xidian University, Xi'an 710071,China
    2. School of Information Science and Technology, Taishan University, Taishan 271000,China
    3. Shandong Chengcheng Internet, Taishan 271000,China
  • Received:2017-10-16 Online:2018-11-20 Published:2018-12-11

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Abstract:

The speckle noise causes the target feature unobvious in the SAR image. The traditional CFAR algorithm only considers the strong scattering feature of object, and it is easily affected by much false alarm target. This paper proposes a novel conjugate CFAR algorithm which is more precise for object detection. The conjugate CFAR algorithm extracts the strong scattering feature as the traditional CFAR algorithm does. Then, the shadow feature detection is implemented by modifying the integral interval of the traditional CFAR algorithm. Therefore, the better results can be achieved by deleting the false alarm target according to the spatial relationship between the strong scattering feature and the shadow feature. The experimental results show that the false alarm rate of the algorithm is much lower than that of the traditional CFAR algorithm at the same detection rate.

Key words: information procession technology, object detecting, image semantic, very high resolution, synthetic aperture radar(SAR) image

CLC Number: 

  • TP391.4

Fig.1

Flow chart of algorithm"

Fig.2

Gray statistics of multiple high-R SAR images"

Fig.3

Target left sight scattering madel"

Table 1

Comparison of detecting results"

算法 图像1 图像2
目标数 检测率/% 虚警目标 虚警率/% 目标数 检测率/% 虚警目标 虚警率/%
耦合CFAR 4 100 2 33 3 100 0 0
传统CFAR 4 100 12 75 3 100 13 81
超像素CFAR 4 100 8 67 3 100 6 67

Fig.4

Detecting results"

[1] 曾丽娜, 周德云, 邢孟道 , 等. 一种多特征联合的地面SAR目标分层检测方法[J]. 西安电子科技大学学报, 2016,43(2):89-94.
Zeng Li-na, Zhou De-yun, Xing Meng-dao , et al. Novel SAR target detection algorithm mvia multiple features[J]. Journal of Xidian University, 2016,43(2):89-94.
[2] Eldarymli K, Mcguire P, Power D , et al. Target detection in synthetic aperture radar imagery: a state-of-the-art survey[J]. Journal of Applied Remote Sensing, 2013,7(1):1-35.
[3] 高贵, 周蝶飞, 蒋咏梅 , 等. SAR图像目标检测研究综述[J]. 信号处理, 2008,24(6):971-981.
doi: 10.3969/j.issn.1003-0530.2008.06.018
Gao Gui, Zhou Die-fei, Jiang Yong-mei , et al. Study on target detection in SAR image:a survey[J]. Signal Processing, 2008,24(6):971-981.
doi: 10.3969/j.issn.1003-0530.2008.06.018
[4] 顾丹丹, 许小剑 . 基于积分图像的快速ACCA-CFAR SAR图像目标检测算法[J]. 系统工程与电子技术, 2014,36(2):248-253.
doi: 10.3969/j.issn.1001-506X.2014.02.08
Gu Dan-dan, Xu Xiao-jian . Fast ACCA-CFAR algorithm based on integral image for target detection from SAR images[J]. Systems Engineering and Electronics, 2014,36(2):248-253.
doi: 10.3969/j.issn.1001-506X.2014.02.08
[5] 何楚, 张宇, 廖紫纤 , 等. 基于压缩感知的SAR图像CFAR目标检测算法[J]. 武汉大学学报:信息科学版, 2014,39(7):878-882.
doi: 10.13203/j.whugis20120693
He Chu, Zhang Yu, Liao Zi-qian , et al. Compressive sensing based CFAR target detection algorithm for SAR image[J]. Geomatics and Information Science of Wuhan University, 2014,39(7):878-882.
doi: 10.13203/j.whugis20120693
[6] Yu Wen-yi, Wang Ying-hua, Liu Hong-wei , 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
[7] Hou Biao, Cheng Xing-zhong, Jiao Li-cheng . 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
[8] Jahangir M, Blacknell D, Moate C P, et al. Extracting information from shadows in SAR imagery [C]//International Conference Machine Vision,Pakistan, 2007: 107-112.
[9] Ferro A, Brunner D, Bruzzone L . Automatic detection and reconstruction of building radar footprints from single VHR SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(2):935-952.
doi: 10.1109/TGRS.2012.2205156
[10] Li Heng-chao, Hong Wen, Wu Yi-rong , et al. On the empirical-statistical modeling of SAR images with generalized gamma distribution[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(3):386-397.
doi: 10.1109/JSTSP.2011.2138675
[11] Sparr T, Hansen R E, Callow H J , et al. Enhancing target shadows in SAR images[J]. Electronics Letters, 2007,43(5):308-309.
doi: 10.1049/el:20073883
[12] Mason D C, Speck R, Devereux B , et al. Flood detection in urban areas using TerraSAR-X[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010,48(2):882-894.
doi: 10.1109/TGRS.2009.2029236
[13] Huang Yong, Liu Fang . Detecting cars in VHR SAR images via semantic CFAR algorithm[J]. IEEE Geoscience and Remote Sensing Letters, 2016,13(6):801-805.
doi: 10.1109/LGRS.2016.2546309
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