DENG Yang-yang,RAN Yuan-bo,HAN Jing-hong.Research on SAR Image Target Detection based on Superpixel and Information Theory[J].Journal of Chengdu University of Information Technology,2017,(05):479-486.[doi:10.16836/j.cnki.jcuit.2017.05.003]
基于超像素和信息论的SAR图像目标检测研究
- Title:
- Research on SAR Image Target Detection based on Superpixel and Information Theory
- 文章编号:
- 2096-1618(2017)05-0479-08
- Keywords:
- SAR; CFAR; superpixels; self-information; weighted information entropy
- 分类号:
- TN957.71
- 文献标志码:
- A
- 摘要:
- 针对传统的恒虚警率(CFAR)检测算法的不足,提出新的基于超像素和信息论的SAR图像目标检测方法。首先利用改进的SLIC超像素生成算法将SAR图像分割成超像素块,然后计算各超像素块自信息值并选出候选超像素块,最后用邻域加权信息熵剔除法滤除虚警超像素块,最终得到目标检测结果。同时与两种基于杂波统计模型的CFAR检测算法的检测效果进行对比,结果表明所提检测算法对SAR目标有较高的检测准确度,且目标物原有形态能得到很好的保留。
- Abstract:
- In order to make up the shortcomings of the traditional CFAR detection algorithm, a new SAR image target detection method based on superpixel and information theory is proposed. Firstly, the SAR image is divided into superpixel patches by using the improved SLIC superpixel generation method, and then the information value of the superpixel patches are calculated and the candidate superpixel patches are selected. Finally, The extravagant patches are filtered by using the neighborhood weighted information entropy rejection method, and the target detection result is obtained. At the same time, comparing the result of the proposed method with the results of two CFAR detection algorithms based on clutter statistical model. The results show that the proposed algorithm has high detection accuracy for SAR image target, and the original shape of target can be well preserved.
参考文献/References:
[1] 焦李成.智能SAR图像处理与解译[M].北京:科学出版社,2008.
[2] 朱良,郭巍,禹卫东.合成孔径雷达卫星发展历程及趋势分析[J].现代雷达,2009,(4):5-10.
[3] Massonnet D,Souyris J C.Imaging with synthetic aperture radar[M].CRC Press,2008.
[4] 复杂场景下的SAR目标检测[D].西安:西安电子科技大学,2015.
[5] 匡纲要.合成孔径雷达目标检测理论、算法及应用[M].长沙:国防科技大学出版社,2007.
[6] Frery A C,Muller H J,Yanasse C C F,et al.A model for extremely heterogeneous clutter[J].IEEE Transactions on Geoscience & Remote Sensing,1997,35(3):648-659.
[7] Anastassopoulos V,Lampropoulos G A.Optimal CFAR detection in Weibull clutter[J].IEEE Transactions on Aerospace and Electronic Systems,1995,31(1):52-64.
[8] Qin X,Zhou S,Zou H,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.
[9] Qin X,Zhou S,Zou H,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.
[10] 赵明波,何峻,付强.SAR图像CFAR检测的快速算法综述[J].自动化学报,2012,38(12):1885-1895.
[11] Feng J,Cao Z,Pi Y.Variational SAR image segmentation based on the G0 model and an augmented Lagrangian method[J].Progress In Electromagnetics Research B,2012,39:373-392.
[12] Novak L M,Burl M C. Optimal Speckle Reduction in Polarimetric SAR Imagery[C].Proc.22nd Asilomar Conf.Singal,System,and Conpnters Paeific,CA,1988:781-785。
[13] 冯籍澜.高分辨率SAR图像分割与分类方法研究[D].成都:电子科技大学,2015.
[14] 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.
[15] Cao Z,Ge Y,Feng J.Fast target detection method for high-resolution SAR images based on variance weighted information entropy[J].EURASIP Journal on Advances in Signal Processing,2014,(1):1.
[16] 杨道莲.机载SAR图像陆地目标检测方法的研究[D].合肥:合肥工业大学,2013.
[17] 庄圆.复杂背景下SAR图像目标特征提取与分析研究[D].哈尔滨:哈尔滨工业大学,2015.
[18] 高贵.高分辨率SAR图像目标特征提取研究[D].北京:国防科学技术大学,2003.
[19] Ritcey J A,Du H. Order statistic CFAR detectors for speckled area targets in SAR[C].Signals,Systems and Computers.Conference Record of the Twenty-Fifth Asilomar Conference on.IEEE,1991:1082-1086.
[20] Rohling H.Radar CFAR thresholding in clutter and multiple target situations[J].IEEE transactions on aerospace and electronic systems,1983,(4):608-621.
[21] 谢堃.合成孔径雷达图像目标检测方法研究[D].南京:南京航空航天大学,2011.
[22] Muller H J.Modeling of extremely heterogeneous radar backscatter[C].Geoscience and Remote Sensing.IGARSS'97.Remote Sensing-A Scientific Vision for Sustainable Development.,1997 IEEE International.IEEE,1997,(4):1603-1605.
备注/Memo
收稿日期:2017-07-02 资助项目:四川省科技计划资助项目(2016JY0106)