Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

Song Tu; Yi Su; Wei Wang; Boli Xiong; Yu Li

doi:10.1117/1.JRS.9.096039

11 June 2015 Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image

Song Tu, Yi Su, Wei Wang, Boli Xiong, Yu Li

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096039 (June 2015). https://doi.org/10.1117/1.JRS.9.096039

Abstract

Existing automatic target recognition of synthetic aperture radar (SAR ATR) schemes mainly focus on target chips, but there is very little research for a large-scale and high-resolution SAR image that is more practical for SAR image interpretation. How to recognize targets efficiently and accurately from a large-scale and high-resolution SAR image is still a challenge. We present a scheme based on the combination of a salient detection approach, an active contour model (ACM), an affine-invariant shape descriptor, and the corresponding shape context. During the detection stage, the spectral residual approach is utilized to efficiently preselect salient regions. The proposed convex ACM, based on a ratio distance and distribution metric which makes it more robust to multiplicative speckled noise, is then adopted to get accurate candidate target chips. For the discrimination stage, a cumulative sum of multiscale lacunarity feature is proposed to select vehicle chips from clutter chips. Finally, affine-invariant shape features, obtained from the contours by our proposed ACM, are combined with a corresponding shape context to make the classification more accurate. Experimental results demonstrate that our SAR ATR system, integrating all the proposed methods, is feasible in ATR from a high-resolution and large-scale SAR image.

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Song Tu, Yi Su, Wei Wang, Boli Xiong, and Yu Li "Automatic target recognition scheme for a high-resolution and large-scale synthetic aperture radar image," Journal of Applied Remote Sensing 9(1), 096039 (11 June 2015). https://doi.org/10.1117/1.JRS.9.096039

Published: 11 June 2015

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