Vehicle detection in synthetic aperture radar images with feature fusion-based sparse representation

Wentao Lv; Lipeng Guo; Weiqiang Xu; Xiaocheng Yang; Long Wu

doi:10.1117/1.JRS.12.025020

30 June 2018 Vehicle detection in synthetic aperture radar images with feature fusion-based sparse representation

Wentao Lv, Lipeng Guo, Weiqiang Xu, Xiaocheng Yang, Long Wu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 2, 025020 (June 2018). https://doi.org/10.1117/1.JRS.12.025020

Abstract

A vehicle detection algorithm is presented for synthetic aperture radar (SAR) images. This method formulates the detection mission within a sparse representation (SR) fusion frame. A set of residuals, for one specific feature, is first generated by performing the sparse reconstructions over dictionaries associated with the available set of possible targets. They are then normalized and further formed into a single residual sequence. After the collection of all residual sequences for all types of features, a linear fusion strategy is applied to the sequences to infer an optimal target estimate. As the final decision is made based on the residual fusion related with the concatenation of multiple features, this algorithm exhibits strong discriminative powers with respect to target confirmation. Moreover, a merging technique is developed to integrate a more accurate region for each vehicle. The test results based on real scene data show that the presented method is superior to some state-of-the-art alternatives.

Citation Download Citation

Wentao Lv, Lipeng Guo, Weiqiang Xu, Xiaocheng Yang, and Long Wu "Vehicle detection in synthetic aperture radar images with feature fusion-based sparse representation," Journal of Applied Remote Sensing 12(2), 025020 (30 June 2018). https://doi.org/10.1117/1.JRS.12.025020

Received: 29 September 2017; Accepted: 15 June 2018; Published: 30 June 2018

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available