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Journal of Applied Remote Sensing

Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning
Author(s): Xunxun Zhang; Hongke Xu; Jianwu Fang; Gang Sheng
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Paper Abstract

Vehicle detection in high-resolution aerial images has received widespread interests when it comes to providing the required information for traffic management and urban planning. It is challenging due to the relatively small size of the vehicles and the complex background. Furthermore, it is particularly challenging if the higher detection efficiency is required. Therefore, an urban vehicle detection algorithm is proposed via improved entropy rate clustering (IERC) and correlation-based sequential dictionary learning (CSDL). First, to enhance the detection accuracy, IERC is designed to generate more regular superpixels. It aims to avoid the situation that one superpixel sometimes straddles multiple vehicles. The generated superpixels are then treated as the seeds for the training sample selection. Then, CSDL is constructed to achieve a fast sequential training and updating of the dictionary. In CSDL, only the atoms correlated with the sparse representation of the new training data are inferred. Finally, comprehensive analyses and comparisons on two data sets demonstrate that the proposed method generates satisfactory and competitive results.

Paper Details

Date Published: 7 June 2017
PDF: 17 pages
J. Appl. Rem. Sens. 11(2) 026028 doi: 10.1117/1.JRS.11.026028
Published in: Journal of Applied Remote Sensing Volume 11, Issue 2
Show Author Affiliations
Xunxun Zhang, Chang'an Univ. (China)
Hongke Xu, Chang'an Univ. (China)
Jianwu Fang, Chang'an Univ. (China)
Gang Sheng, Research Institute of Highway (China)
Ministry of Transport (China)

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