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Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features

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Geo-spatial Information Science

Abstract

Designing detection algorithms with high efficiency for Synthetic Aperture Radar (SAR) imagery is essential for the operator SAR Automatic Target Recognition (ATR) system. This work abandons the detection strategy of visiting every pixel in SAR imagery as done in many traditional detection algorithms, and introduces the gridding and fusion idea of different texture features to realize fast target detection. It first grids the original SAR imagery, yielding a set of grids to be classified into clutter grids and target grids, and then calculates the texture features in each grid. By fusing the calculation results, the target grids containing potential maneuvering targets are determined. The dual threshold segmentation technique is imposed on target grids to obtain the regions of interest. The fused texture features, including local statistics features and Gray-Level Co-occurrence Matrix (GLCM), are investigated. The efficiency and superiority of our proposed algorithm were tested and verified by comparing with existing fast detection algorithms using real SAR data. The results obtained from the experiments indicate the promising practical application value of our study.

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Authors and Affiliations

  1. Research Institute of Information Fusion Technology, Naval Aeronautical and Astronautical University, 128 Erma Road, Yantai, 264001, China

    Zhan Yuan, You He & Fuqing Cai

Authors
  1. Zhan Yuan
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  2. You He
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  3. Fuqing Cai
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Corresponding author

Correspondence to Zhan Yuan.

Additional information

Supported by the National Natural Science Foundation of China (No. 61032001, No.61002045).

YUAN Zhan, Ph.D. candidate. His research interests are pattern recognition, remote sensing data processing and multisensor information fusion.

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Yuan, Z., He, Y. & Cai, F. Fast algorithm for maneuvering target detection in SAR imagery based on gridding and fusion of texture features. Geo-spat. Inf. Sci. 14, 169–176 (2011). https://doi.org/10.1007/s11806-011-0536-6

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  • DOI: https://doi.org/10.1007/s11806-011-0536-6

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