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Proceedings Paper

Target detection from SAR images based on wavelet transform de-noise and improved CFAR
Author(s): Bo Zhao; Li Chen; Xiao Yang Zhou; Xin Yi He; Shu Run Tan; Hai Lin; Tie Jun Cui
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Paper Abstract

Target detection is an important part of an automatic target recognition (ATR) system. There would be many false alarms if using constant false alarm rate (CFAR) algorithm directly on complex synthetic aperture radar (SAR) images with tremendous speckle. Usually, the speckle should be reduced previously before CFAR. In this paper, a wavelet transform de-noise and an improved CFAR algorithm have been combined to detect military targets from SAR image. Different threshold methods were used in the wavelet domain when dealing with the detail information and non-detail information in the image to receive the edge information and reduce the speckle. Then a three-stage CFAR algorithm was used to detect the de-noised image. This algorithm contains global CFAR, local CFAR and count filters. Good results are obtained when the method is used to process high-resolution, HH polarization SAR images. Such algorithms could be arranged in the SAR image based automatic target recognition system.

Paper Details

Date Published: 30 October 2009
PDF: 6 pages
Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749539 (30 October 2009); doi: 10.1117/12.832985
Show Author Affiliations
Bo Zhao, Southeast Univ. (China)
Li Chen, Zhejiang Univ. (China)
Xiao Yang Zhou, Southeast Univ. (China)
Xin Yi He, Southeast Univ. (China)
Shu Run Tan, Southeast Univ. (China)
Hai Lin, Zhejiang Univ. (China)
Tie Jun Cui, Southeast Univ. (China)


Published in SPIE Proceedings Vol. 7495:
MIPPR 2009: Automatic Target Recognition and Image Analysis

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