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Ship detection based on rotation-invariant HOG descriptors for airborne infrared images
Author(s): Guojing Xu; Jinyan Wang; Shengxiang Qi
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Paper Abstract

Infrared thermal imagery is widely used in various kinds of aircraft because of its all-time application. Meanwhile, detecting ships from infrared images attract lots of research interests in recent years. In the case of downward-looking infrared imagery, in order to overcome the uncertainty of target imaging attitude due to the unknown position relationship between the aircraft and the target, we propose a new infrared ship detection method which integrates rotation invariant gradient direction histogram (Circle Histogram of Oriented Gradient, C-HOG) descriptors and the support vector machine (SVM) classifier. In details, the proposed method uses HOG descriptors to express the local feature of infrared images to adapt to changes in illumination and to overcome sea clutter effects. Different from traditional computation of HOG descriptor, we subdivide the image into annular spatial bins instead of rectangle sub-regions, and then Radial Gradient Transform (RGT) on the gradient is applied to achieve rotation invariant histogram information. Considering the engineering application of airborne and real-time requirements, we use SVM for training ship target and non-target background infrared sample images to discriminate real ships from false targets. Experimental results show that the proposed method has good performance in both the robustness and run-time for infrared ship target detection with different rotation angles.

Paper Details

Date Published: 8 March 2018
PDF: 7 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060912 (8 March 2018); doi: 10.1117/12.2285307
Show Author Affiliations
Guojing Xu, Science and Technology on Avionics Integration Lab. (China)
China National Aeronautical Radio Electronics Research Institute (China)
Jinyan Wang, Science and Technology on Avionics Integration Lab. (China)
China National Aeronautical Radio Electronics Research Institute (China)
Shengxiang Qi, Science and Technology on Avionics Integration Lab. (China)
China National Aeronautical Radio Electronics Research Institute (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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