Fast target detection method for high-resolution SAR images based on variance weighted information entropy

Since the traditional CFAR algorithm is not suitable for high-resolution target detection of synthetic aperture radar (SAR) images, a new two-stage target detection method based on variance weighted information entropy is proposed in this paper. On the first stage, the regions of interest (ROIs) in SAR image is extracted based on the variance weighted information entropy (WIE), which has been proved to be a simple and effective quantitative description index for the complex degree of infrared image background. Considering that SAR images are nonuniform, an experiment is conducted ahead, in which the value of the variance WIE from a real SAR image in three areas with significant different uniform levels are tested and compared. The results preliminarily verified that the variance WIE is able to measure the complex degree of SAR images. After that, in order to make the segmentation efficient, the rough ROIs are further processed with a series of methods which adjust ROIs into regular pieces. On the second stage, for each of the ROIs, a variational segmentation algorithm based on the Split-Bregman algorithm is adopted to extract the target. In our experiment, the proposed method is tested on two kinds of SAR images, and its effectiveness is successfully demonstrated.

[1]  Lei Yang,et al.  Variance WIE based infrared images processing , 2006 .

[2]  D S Shin,et al.  Segmentation and surface reconstruction of a cadaver heart on Mimics software. , 2015, Folia morphologica.

[3]  Pramod K. Varshney,et al.  CFAR detection for multiple target situations , 1989 .

[4]  Ying Li,et al.  Fast and accuracy extraction of infrared target based on Markov random field , 2011, Signal Process..

[5]  J. Saniie,et al.  Performance analysis of linearly combined order statistic CFAR detectors , 1995 .

[6]  Xavier Bresson,et al.  Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction , 2010, J. Sci. Comput..

[7]  Hermann Rohling,et al.  Radar CFAR Thresholding in Clutter and Multiple Target Situations , 1983, IEEE Transactions on Aerospace and Electronic Systems.

[8]  Yiming Pi,et al.  A Variational Level Set SAR Image Segmentation Approach Based on Statistical Model , 2008 .

[9]  Y. Pi,et al.  A variational level set approach for automatic target extraction of SAR images , 2007, 2007 1st Asian and Pacific Conference on Synthetic Aperture Radar.

[10]  Jie Yang,et al.  New criterion to evaluate the complex degree of sea-sky infrared backgrounds , 2005 .

[11]  PI Yi-ming.  A Level Set Approach for SAR Image Segmentation Based on G~0 Distribution , 2010 .

[12]  Lei Yang,et al.  Adaptive detection for infrared small target under sea-sky complex background , 2004 .

[13]  Cao Ning-sheng,et al.  Radar Signal Sorting Method Based on Set Pair Analysis , 2010 .

[14]  Tom Goldstein,et al.  The Split Bregman Method for L1-Regularized Problems , 2009, SIAM J. Imaging Sci..

[15]  Chang-Joo Kim,et al.  Generalized OS CFAR detector with noncoherent integration , 1993, Signal Process..

[16]  H. M. Finn,et al.  Adaptive detection mode with threshold control as a function of spatially sampled clutter level estimates , 1968 .

[17]  H. Goldman,et al.  Performance of the excision CFAR detector in the presence of interferers , 1990 .

[18]  I. Ozgunes,et al.  A variably trimmed mean CFAR radar detector , 1992 .

[19]  Yiming Pi,et al.  Variational SAR Image Segmentation Based on the G0 Model and an Augmented Lagrangian Method , 2012 .