Vehicle Detection in High-Resolution Aerial Images Based on Fast Sparse Representation Classification and Multiorder Feature

被引:52
作者
Chen, Ziyi [1 ]
Wang, Cheng [1 ]
Luo, Huan [1 ]
Wang, Hanyun [2 ]
Chen, Yiping [1 ]
Wen, Chenglu [1 ]
Yu, Yongtao [1 ]
Cao, Liujuan [1 ]
Li, Jonathan [3 ]
机构
[1] Xiamen Univ, Sch Informat Sci & Engn, Fujian Key Lab Sensing & Comp Smart City, Xiamen 361005, Peoples R China
[2] Natl Univ Def Technol, Coll Elect Sci & Engn, Changsha 410073, Hunan, Peoples R China
[3] Univ Waterloo, Fac Environm, Dept Geog & Environm Management, Waterloo, ON N2L 3G1, Canada
基金
中国国家自然科学基金;
关键词
Vehicle detection; multiorder feature; sparse representation; superpixel segmentation; aerial image; OBJECT DETECTION; CAR DETECTION;
D O I
10.1109/TITS.2016.2517826
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper presents an algorithm for vehicle detection in high-resolution aerial images through a fast sparse representation classification method and amultiorder feature descriptor that contains information of texture, color, and high-order context. To speed up computation of sparse representation, a set of small dictionaries, instead of a large dictionary containing all training items, is used for classification. To extract the context information of a patch, we proposed a high-order context information extraction method based on the proposed fast sparse representation classification method. To effectively extract the color information, the RGB color space is transformed into color name space. Then, the color name information is embedded into the grids of histogram of oriented gradient feature to represent the low-order feature of vehicles. By combining low- and high-order features together, a multiorder feature is used to describe vehicles. We also proposed a sample selection strategy based on our fast sparse representation classification method to construct a complete training subset. Finally, a set of dictionaries, which are trained by the multiorder features of the selected training subset, is used to detect vehicles based on superpixel segmentation results of aerial images. Experimental results illustrate the satisfactory performance of our algorithm.
引用
收藏
页码:2296 / 2309
页数:14
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