Volume 45 Issue 1
Feb.  2016
Turn off MathJax
Article Contents

Zhang Difei, Zhang Jinsuo, Yao Keming, Cheng Minwei, Wu Yongguo. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 104004-0104004(6). doi: 10.3788/IRLA201645.0104004
Citation: Zhang Difei, Zhang Jinsuo, Yao Keming, Cheng Minwei, Wu Yongguo. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 104004-0104004(6). doi: 10.3788/IRLA201645.0104004

Infrared ship-target recognition based on SVM classification

doi: 10.3788/IRLA201645.0104004
  • Received Date: 2015-05-05
  • Rev Recd Date: 2015-06-13
  • Publish Date: 2016-01-25
  • Aiming at the ship-target recognition of sea-sky background, an classification algorithm based on machine learning was proposed. In the method, the segmentation algorithm was firstly adopted to extract connected region in infrared image. Then, the corresponding position of the original image was marked and normalized. Afterwards, the high-dimensional feature vector of branded region by using the HOG algorithm was extracted. Finally, the high-dimensional feature vector that came form suspected target area was classified by the SVM classifier which was trained by sample library. Simulation experimental result indicates that the algorithm not only can effectively recognise the infrared ship-targets in complex sea-sky background of interference, but also have good performance.
  • [1] Christopher M Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics)[M]. New York:Springer, 2006.
    [2] Vpnik V N, Chervonenkis A Ja. Theoey of Pattern Recognition[M]. New York: Springer, 1974.
    [3] Yu Youchuan. Vehicle recognition method based on machine learning[D]. Xi'an: Xidian University, 2009. (in Chinese) 于筱川。基于机器学习的车辆目标识别方法[D]. 西安: 西安电子科技大学, 2009.
    [4] Zhang Quanfa, Pu Baoming, Li Tianran, et al. Vehicles detection based on histograms of oriented gradients and machine learning[J]. Computer Systems Applications, 2013, 22(7): 104-107. (in Chinese) 张全发, 蒲宝明, 李天然, 等。基于HOG特征和机器学习的工程车辆检测[J]. 计算机系统应用, 2013, 22(7): 104-107.
    [5] Mu Chunlei. The research of face recognition system based on HOG feature[D]. Chengdu: University of Electronic Science and Technology of China, 2013. (in Chinese) 慕春雷。基于HOG特征的人脸识别系统研究[D]. 成都: 电子科技大学, 2013.
    [6] Zhao Lei, Wang Bing, Zhang Liming. Change detection for remotely sensed images based on split window and semi-supervised SVM[J]. Journal of Fudan University(Natural Science), 2010, 49(2): 190-196. (in Chinese) 赵磊, 王斌, 张立明。基于分割窗半监督支持向量机的遥感图像变化检测[J]. 复旦学报(自然科学版), 2010, 49(2): 190-196.
    [7] Wang Peng, Lv Gaojie, Gong Junbin, et al. An automatic target detection method for infrared ship in complex sea-sky background[J]. Geomatics and Information Science of Wuhan University, 2011, 36(12): 1438-1441. (in Chinese) 王鹏, 吕高杰, 龚俊斌, 等。一种复杂海天背景下的红外舰船目标自动检测方法[J]. 武汉大学学报信息科学版, 2011, 36(12): 1438-1441. (in Chinese)
    [8] Dalal Navneet, Triggs Bill. Histograms of oriented gradients for human detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, 1: 886-893.
    [9] Carl Vondrick, Aditya Khosla, Tomasz Malisiewicz, et al. HOGgles: Visualizing Object Detection Features[C]// 2013 IEEE International Conference on Computer Vision (ICCV), 2013, 1: 1-8.
    [10] Cortes C, Vapnik V N. Support vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
    [11] Webb G I, Ting K M. On the application of ROC analysis to predict classification performance under varying class distribution [J]. Machine Learning, 2005, 58(1): 2.
  • 加载中
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

Article Metrics

Article views(898) PDF downloads(488) Cited by()

Related
Proportional views

Infrared ship-target recognition based on SVM classification

doi: 10.3788/IRLA201645.0104004
  • 1. Tianjin Jinhang Institute of Technical Physics,Tianjin 300192,China

Abstract: Aiming at the ship-target recognition of sea-sky background, an classification algorithm based on machine learning was proposed. In the method, the segmentation algorithm was firstly adopted to extract connected region in infrared image. Then, the corresponding position of the original image was marked and normalized. Afterwards, the high-dimensional feature vector of branded region by using the HOG algorithm was extracted. Finally, the high-dimensional feature vector that came form suspected target area was classified by the SVM classifier which was trained by sample library. Simulation experimental result indicates that the algorithm not only can effectively recognise the infrared ship-targets in complex sea-sky background of interference, but also have good performance.

Reference (11)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return