Ground Penetrating Radar Signal Processing Based on Morphological Component Analysis

Ground-penetrating radar (GPR) is one of the most popular underground detection devices and has a wide range of applications. However, when using GPR to detect targets, since targets are located near the surface, the influence of clutter on target detection is very serious. Especially in some complex environments, targets may be completely covered by clutter. Thus, clutter reduction is the primary task. Singular value decomposition (SVD), principal component analysis (PCA) and independent component analysis (ICA) are commonly used for target detection. In this paper, a method based on morphological component analysis (MCA) is adopted, and a decomposition model is proposed to distinguish between target and clutter. Finally, it is proved by visual simulation that this method is superior to other methods in removing clutter.

[1]  Zihan Zhou,et al.  Demo: Robust face recognition via sparse representation , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[2]  Michael Elad,et al.  Image Denoising Via Learned Dictionaries and Sparse representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[3]  J. Bobin,et al.  Morphological component analysis , 2005, SPIE Optics + Photonics.

[4]  D. Donoho,et al.  Redundant Multiscale Transforms and Their Application for Morphological Component Separation , 2004 .

[5]  Xiong Chen,et al.  Satellite Image Classification Using Morphological Component Analysis of Texture and Cartoon Layers , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Michael Elad,et al.  Submitted to Ieee Transactions on Image Processing Image Decomposition via the Combination of Sparse Representations and a Variational Approach , 2022 .

[7]  E. Candès,et al.  Curvelets: A Surprisingly Effective Nonadaptive Representation for Objects with Edges , 2000 .

[8]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[9]  Michael Elad,et al.  Morphological diversity and source separation , 2006, IEEE Signal Processing Letters.

[10]  D. Donoho,et al.  Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA) , 2005 .

[11]  Ruqiang Yan,et al.  Wavelets: Theory and Applications for Manufacturing , 2010 .

[12]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[13]  Ajay Kumar,et al.  Computer-Vision-Based Fabric Defect Detection: A Survey , 2008, IEEE Transactions on Industrial Electronics.

[14]  Bo Gao,et al.  Evaluation of errors induced by soil dielectric models for soil moisture retrieval at L-band , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[15]  N. H. C. Yung,et al.  Automated fabric defect detection - A review , 2011, Image Vis. Comput..