Rotation Sliding Window of the Hog Feature in Remote Sensing Images for Ship Detection

被引:10
作者
Gan, Lu [1 ]
Liu, Peng [2 ]
Wang, Lizhe [2 ]
机构
[1] Univ Sci & Technol China, Sch Informat Sci & Technol, Hefei 230027, Anhui, Peoples R China
[2] Chinese Acad Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100094, Peoples R China
来源
2015 8TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1 | 2015年
基金
中国国家自然科学基金;
关键词
Hog Feature; Support Vector Machine; Rotate Images; Ship Detection;
D O I
10.1109/ISCID.2015.248
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Ship detection plays a relatively vital role in the effect of the traditional of military. In remote sensing image, we combined Histograms of Oriented Gradients features and support vector machine for ship detection. However, hog feature does not have a rotation of invariant, ship can be in any direction. Consequently, in this paper, we proposes a measure of continuous interval rotating detection sliding window of hog feature. We extract and train hog feature of positive and negative samples. Then, continuous interval rotating sliding window of hog feature to improve the accuracy of detecting ship. The experiments reveal that the detection rate can reach high of 72.7% in the vertical direction of test ship. It is of practical significance for civil and military field.
引用
收藏
页码:401 / 404
页数:4
相关论文
共 11 条
  • [1] Bao X. X., 2012, P 33 WIC S INF THEOR
  • [2] Bloisi D., 2012, 2012 15th International Conference on Information Fusion (FUSION 2012), P1982
  • [3] A complete processing chain for ship detection using optical satellite imagery
    Corbane, Christina
    Najman, Laurent
    Pecoul, Emilien
    Demagistri, Laurent
    Petit, Michel
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (22) : 5837 - 5854
  • [4] Histograms of oriented gradients for human detection
    Dalal, N
    Triggs, B
    [J]. 2005 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, VOL 1, PROCEEDINGS, 2005, : 886 - 893
  • [5] Loomans MJH, 2013, IEEE IMAGE PROC, P4117, DOI 10.1109/ICIP.2013.6738848
  • [6] A cascade of boosted generative and discriminative classifiers for vehicle detection
    Negri, Pablo
    Clady, Xavier
    Hanif, Shehzad Muhammad
    Prevost, Lionel
    [J]. EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2008, 2008 (1)
  • [7] Suzuki S, 2014, 2014 10TH FRANCE-JAPAN/ 8TH EUROPE-ASIA CONGRESS ON MECATRONICS (MECATRONICS), P93, DOI 10.1109/MECATRONICS.2014.7018610
  • [8] Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine
    Tang, Jiexiong
    Deng, Chenwei
    Huang, Guang-Bin
    Zhao, Baojun
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (03): : 1174 - 1185
  • [9] Robust real-time face detection
    Viola, P
    Jones, MJ
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2004, 57 (02) : 137 - 154
  • [10] Wijnhoven R., 2010, Thirty-first Symposium on Information Theory in the Benelux, P73