采用目标背景建模的毫米波弱小目标检测

高志升; 耿龙; 张铖方; 胡占强

doi:10.3788/OPE.20162410.2601

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采用目标背景建模的毫米波弱小目标检测

信息科学 | 更新时间：2020-07-07

- 采用目标背景建模的毫米波弱小目标检测
- Millimeter wave dim small target detection based on target and background modeling
- 光学精密工程 2016年24卷第10期页码：2601-2611
- 作者机构：
  
  西华大学无线电管理技术研究中心, 四川成都 610039
- 作者简介：
  
  高志升(1977-),男,四川万源人,博士,副教授,硕士生导师,2012年四川大学获博士学位,主要从事图像处理、机器学习等方面的研究。E-mail:gzs_xihua@mail.xhu.edu.cn
  [ "耿龙(1988-),男,山东济宁人,硕士,2013年于济宁学院获得学士学位,主要从事模式识别与图像处理方面的研究。E-mail:genglong2017@126.com" ]
- 基金信息：
  
  宇航动力学国家重点实验室开放基金资助项目(2015ADL-DW0302);四川省教育厅重点项目(14ZA0118);国家自然科学基金资助项目(61472328)
- DOI：10.3788/OPE.20162410.2601
  中图分类号： TP391.4
- 收稿日期：2016-06-12，
  
  录用日期：2016-8-12，
  
  纸质出版日期：2016-10
- 稿件说明：
移动端阅览
高志升, 耿龙, 张铖方, 等. 采用目标背景建模的毫米波弱小目标检测[J]. 光学精密工程, 2016,24(10):2601-2611.

Zhi-sheng Gao, Long GENG, Cheng-fang ZHANG, et al. Millimeter wave dim small target detection based on target and background modeling[J]. Optics and precision engineering, 2016, 24(10): 2601-2611.
高志升, 耿龙, 张铖方, 等. 采用目标背景建模的毫米波弱小目标检测[J]. 光学精密工程, 2016,24(10):2601-2611. DOI： 10.3788/OPE.20162410.2601.

Zhi-sheng Gao, Long GENG, Cheng-fang ZHANG, et al. Millimeter wave dim small target detection based on target and background modeling[J]. Optics and precision engineering, 2016, 24(10): 2601-2611. DOI： 10.3788/OPE.20162410.2601.

摘要

基于被动毫米波成像特性，提出了改进的稀疏表示——圆周中心差（ISR-CSCD）算法来解决被动毫米波图像中弱小目标与背景区分度较弱，目标可提取特征较少的问题。该算法通过改进稀疏表示方法完成背景抑制与目标增强。依据目标与周围背景特征先验，提出了圆周中心差背景抑制算法对检测图像进行背景抑制。然后，融合改进稀疏表示方法和圆周中心差背景抑制算法的结果得到抑制了背景的目标增强图像。最后，基于恒虚警率的检测方法完成了弱小目标的检测。对不同场景下的毫米波图像进行了实验检测，结果表明，与主流算法图像稀疏表示（SR）法、鲁棒规则核回归牛顿算法（NRRKR），空时联合分类稀疏表示算法（STCSR）和累积中心与周边差异测量算法（ACSDM）相比，ISR-CSCD算法具有更低的虚警率、更高的检测精度、更强的鲁棒性。对各种虚警率、信噪比之下的毫米波弱小目标检测结果显示，ISR-CSCD检测率相对于其它算法平均提高了约15%。

Abstract

On the basis of characteristics of Passive Millimeter Wave (PMMW) imaging

an Improved Sparse Representation-Circle-Surround Center Difference(ISR-CSCD) algorithm is proposed to improve the weaker distinction between dim small target and background and the smaller target features to be extracted. The algorithm firstly improves the sparse representation to complete the background suppression and target enhancement. Then

according to the features and prior knowledge of the target and the surrounding background

the background suppression algorithm of circle-surround center difference is used to suppress the background of the image. The results by two methods mentioned above are fused to get the final enhanced target image. Finally

the Constant False Alarm Rate (CFAR) is used to complete dim small target extraction. The millimeter wave images in different scenes are detected. The results show that as compared with the mainstream algorithms

Sparse representation (SR)

Newton methods for Robust Regularized Kernel Regression(NRRKR)

Spatio-temporal Classification Sparse Representation(STCSR) and Accumulated Center-surround Difference Measurement(ACSDM)

the ISR-CSCD algorithm has a lower false alarm rate

higher detection accuracy and stronger robustness. For all kinds of false alarm rates and the signal to noise ratios of the millimeter wave small target detection results in statistics

the detection rate of ISR-CSCD is increased by about 15% as compared with other algorithms.

关键词

Keywords

references

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