Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning

Xunxun Zhang; Hongke Xu; Jianwu Fang; Gang Sheng

doi:10.1117/1.JRS.11.026028

7 June 2017 Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning

Xunxun Zhang, Hongke Xu, Jianwu Fang, Gang Sheng

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 2, 026028 (June 2017). https://doi.org/10.1117/1.JRS.11.026028

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.

Citation Download Citation

Xunxun Zhang, Hongke Xu, Jianwu Fang, and Gang Sheng "Urban vehicle detection in high-resolution aerial images via superpixel segmentation and correlation-based sequential dictionary learning," Journal of Applied Remote Sensing 11(2), 026028 (7 June 2017). https://doi.org/10.1117/1.JRS.11.026028

Received: 20 December 2016; Accepted: 17 April 2017; Published: 7 June 2017

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