Efficient Clutter Suppression in SAR Images With Shedding Irrelevant Patterns

Strong clutter reflections of terrain and marine surfaces obscure the contrast between the target-of-interest and clutter (terrain and marine surface reflections) in synthetic aperture radar (SAR) images and consequently hinder the efficiency of image interpretation and analysis. To overcome this problem, this letter proposes an efficient clutter suppression method in SAR images, which is named shedding irrelevant patterns (SIP). The essence is to construct a regression function that can suppress clutter and preserve the target patterns concurrently. We assume that the clutter is irrelevant to the target-of-interest and distinguishable in patterns in terms of image-pixel distribution and intensity (spatial information). Experimental results show the efficiency of the proposed method in both clutter suppression and target pattern preservation.

[1]  WenSen Feng,et al.  SAR Image Despeckling Using Data-Driven Tight Frame , 2014, IEEE Geoscience and Remote Sensing Letters.

[2]  Michael Lee Bryant,et al.  Standard SAR ATR evaluation experiments using the MSTAR public release data set , 1998, Defense, Security, and Sensing.

[3]  J. Jennifer Ranjani,et al.  Dual-Tree Complex Wavelet Transform Based SAR Despeckling Using Interscale Dependence , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Mengdao Xing,et al.  Robust Clutter Suppression and Moving Target Imaging Approach for Multichannel in Azimuth High-Resolution and Wide-Swath Synthetic Aperture Radar , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Emmanuel J. Candès,et al.  The curvelet transform for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[8]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[9]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Guisheng Liao,et al.  Robust Ambiguous Clutter Suppression for the Near-Shore Water Areas With Spaceborne Multichannel SAR Systems , 2015, IEEE Geoscience and Remote Sensing Letters.

[11]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[12]  Alexander A. Sawchuk,et al.  Adaptive Restoration Of Images With Speckle , 1983, Optics & Photonics.

[13]  Jonathan Li,et al.  SAR Image Denoising via Clustering-Based Principal Component Analysis , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Long Zhao,et al.  SAR Image Filtering Based on the Cauchy–Rayleigh Mixture Model , 2014, IEEE Geoscience and Remote Sensing Letters.

[15]  Cristian Munteanu,et al.  Supervised Constrained Optimization of Bayesian Nonlocal Means Filter With Sigma Preselection for Despeckling SAR Images , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Aamir Saeed Malik,et al.  Subspace-Based Technique for Speckle Noise Reduction in SAR Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Mats I. Pettersson,et al.  Suppression of Clutter in Multichannel SAR GMTI , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[19]  W. Brown.  Synthetic Aperture Radar , 1967, IEEE Transactions on Aerospace and Electronic Systems.

[20]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.