An Improved Fuzzy Region Competition-Based Framework for the Multiphase Segmentation of SAR Images

The objective of this article is to investigate a multiphase segmentation framework for synthetic aperture radar (SAR) images, which is proposed based on the idea of the fuzzy region competition-based method. The fuzzy region competition-based framework is highly efficient and can attain good segmentation performances for conventional images. The framework is achieved based on its convexity, which not only ensures the existence of a globally optimized solution but also enables the convex optimization theory-based solving algorithms that are feasible. However, the constraint conditions of the framework that guarantee this convexity probably cannot be satisfied in the segmentation of images corrupted with strong noise. Therefore, applying this method to an SAR image probably produces an unsatisfactory segmentation result. To address this problem, we propose an improved fuzzy region competition-based framework in terms of the hierarchical strategy, such that the framework is always convex during the iterative calculation. The proposed framework inherits the advantages of the fuzzy region competition-based method, as well as that it is able to be applied to the segmentation of images with strong noise. Several experiments are then carried out to test and verify the performance and the robustness of the proposed framework. It demonstrates that the proposed segmentation framework can be applied to various types of SAR images and achieves satisfactory segmentation results.

[1]  Laurent D. Cohen,et al.  On active contour models and balloons , 1991, CVGIP Image Underst..

[2]  Matthew S. Horritt,et al.  A statistical active contour model for SAR image segmentation , 1999, Image Vis. Comput..

[3]  David A. Clausi,et al.  Unsupervised Polarimetric SAR Image Segmentation and Classification Using Region Growing With Edge Penalty , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Tony F. Chan,et al.  A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model , 2002, International Journal of Computer Vision.

[5]  Xavier Bresson,et al.  Fast Global Minimization of the Active Contour/Snake Model , 2007, Journal of Mathematical Imaging and Vision.

[6]  P. Sivakumar,et al.  A REVIEW ON IMAGE SEGMENTATION TECHNIQUES , 2016 .

[7]  Jitendra Malik,et al.  Region-Based Convolutional Networks for Accurate Object Detection and Segmentation , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Alejandro C. Frery,et al.  The polarimetric 𝒢 distribution for SAR data analysis , 2005 .

[9]  Yan Chen,et al.  A Multi-Region Segmentation Method for SAR Images Based on the Multi-Texture Model With Level Sets , 2018, IEEE Transactions on Image Processing.

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

[11]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[12]  J. Sethian,et al.  Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations , 1988 .

[13]  S Y Luo,et al.  SAR image segmentation based on the advanced level set , 2014 .

[14]  Kamal Sarabandi,et al.  Unsupervised Multiregion Partitioning of Fully Polarimetric SAR Images With Advanced Fuzzy Active Contours , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Chunming Li,et al.  A Variational Level Set Approach to Segmentation and Bias Correction of Images with Intensity Inhomogeneity , 2008, MICCAI.

[16]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[17]  Rama Chellappa,et al.  Segmentation of polarimetric synthetic aperture radar data , 1992, IEEE Trans. Image Process..

[18]  Yan Nei Law,et al.  A Multiresolution Stochastic Level Set Method for Mumford–Shah Image Segmentation , 2008, IEEE Transactions on Image Processing.

[19]  Corina da Costa Freitas,et al.  A model for extremely heterogeneous clutter , 1997, IEEE Trans. Geosci. Remote. Sens..

[20]  Josiane Zerubia,et al.  A Level Set Model for Image Classification , 1999, International Journal of Computer Vision.

[21]  Michael K. Ng,et al.  A Multiphase Image Segmentation Method Based on Fuzzy Region Competition , 2010, SIAM J. Imaging Sci..

[22]  Gui Gao,et al.  Statistical Modeling of SAR Images: A Survey , 2010, Sensors.

[23]  Ron Kwok,et al.  Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution , 1994 .

[24]  Xuelong Li,et al.  An Efficient MRF Embedded Level Set Method for Image Segmentation , 2015, IEEE Transactions on Image Processing.

[25]  Thomas Brox,et al.  Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Level Set Segmentation with Multiple Regions Level Set Segmentation with Multiple Regions , 2022 .

[26]  Patrick Siarry,et al.  Multiobjective improved spatial fuzzy c-means clustering for image segmentation combining Pareto-optimal clusters , 2016, J. Heuristics.

[27]  Jean-Marie Nicolas,et al.  Unsupervised Synthetic Aperture Radar Image Segmentation Using Fisher Distributions , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[28]  M. Nikolova.  An Algorithm for Total Variation Minimization and Applications , 2004 .

[29]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[30]  Jian Yang,et al.  A Modified Level Set Approach for Segmentation of Multiband Polarimetric SAR Images , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Chunming Li,et al.  Level set evolution without re-initialization: a new variational formulation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Amar Mitiche,et al.  Unsupervised Variational Image Segmentation/Classification Using a Weibull Observation Model , 2006, IEEE Transactions on Image Processing.

[33]  S. Osher,et al.  Algorithms Based on Hamilton-Jacobi Formulations , 1988 .

[34]  Hong Sun,et al.  SAR Image Segmentation Based on Level Set With Stationary Global Minimum , 2008, IEEE Geoscience and Remote Sensing Letters.

[35]  Amar Mitiche,et al.  Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Benoit Mory,et al.  Fuzzy Region Competition: A Convex Two-Phase Segmentation Framework , 2007, SSVM.

[37]  Aboul Ella Hassanien,et al.  Adaptive k-means clustering algorithm for MR breast image segmentation , 2013, Neural Computing and Applications.

[38]  Jerry L. Prince,et al.  Snakes, shapes, and gradient vector flow , 1998, IEEE Trans. Image Process..

[39]  Amar Mitiche,et al.  Multiregion level-set partitioning of synthetic aperture radar images , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[40]  Jean-Philippe Thiran,et al.  Variational Segmentation using Fuzzy Region Competition and Local Non-Parametric Probability Density Functions , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[41]  Alan L. Yuille,et al.  Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..