A CFAR algorithm for layover and shadow detection in InSAR images based on kernel density estimation

In this paper, a novel CFAR algorithm for detecting layover and shadow areas in Interferometric synthetic aperture radar (InSAR) images is proposed. Firstly, the probability density function (PDF) of the square root amplitude of InSAR image is estimated by the kernel density estimation. Then, a CFAR algorithm combining with the morphological method for detecting both layover and shadow is presented. Finally, the proposed algorithm is evaluated on a real InSAR image obtained by TerraSAR-X system. The experimental results have validated the effectiveness of the proposed method.

[1]  F. Gini,et al.  Layover solution in multibaseline INSAR using robust beamforming , 2003, Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology (IEEE Cat. No.03EX795).

[2]  Gui Gao,et al.  A Parzen-Window-Kernel-Based CFAR Algorithm for Ship Detection in SAR Images , 2011, IEEE Geoscience and Remote Sensing Letters.

[3]  D. Pairman,et al.  Efficient calculation in the map domain of SAR layover and shadow masks , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[4]  Zou Huanxin,et al.  Layover and shadow detection based on distributed spaceborne single-baseline InSAR , 2014 .

[5]  Giampaolo Ferraioli,et al.  Layover Solution in SAR Imaging: A Statistical Approach , 2009, IEEE Geoscience and Remote Sensing Letters.

[6]  Jian Yang,et al.  New CFAR target detector for SAR images based on kernel density estimation and mean square error distance , 2012 .

[7]  Joseph A. O'Sullivan,et al.  Quantitative statistical assessment of conditional models for synthetic aperture radar , 2004, IEEE Transactions on Image Processing.