A Multi-Region Segmentation Method for SAR Images Based on the Multi-Texture Model With Level Sets

Abstract

Synthetic Aperture Radar (SAR) image segmentation is a difficult problem due to the presence of strong multiplicative noise. To attain multi-region segmentation for SAR images, this paper presents a parametric segmentation method based on the multi-texture model with level sets. Segmentation is achieved by solving level set functions obtained from minimizing the proposed energy functional. To fully utilize image information, edge feature and region information are both included in the energy functional. For the need of level set evolution, the Ratio of Exponentially Weighted Averages (ROEWA) operator is modified to obtain edge feature. Region information is obtained by the Improved Edgeworth Series Expansion (IESE), which can adaptively model a SAR image distribution with respect to various kinds of regions. The performance of the proposed method is verified by three high resolution SAR images. The experimental results demonstrate that SAR images can be segmented into multiple regions accurately without any speckle pre-processing steps by the proposed method.

Structure Label Matrix Completion for PolSAR Image Classification

“…Spectral clustering was used for various polarimetric decomposition results by [6,35,36]. In addition, image-segmentation methods such as level set [37] and simple linear iterative clustering [38] were developed for unsupervised PolSAR image classification.…”

An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

“…Among them, active contour models (ACMs) based on level set theory have become a successful branch. According to the nature of constraints, ACMs can be approximately categorized into two types: edge-based models [11][12][13][14] and region-based models [15][16][17][18]. Edge-based methods rely on local edge information to evolve contour curves towards the target boundaries.…”

Multi-Resolution Texture-Based 3D Level Set Segmentation

“…Typically, these methods use a single feature [11], [14], [15] or ad hoc combinations of descriptors [16], [17]. Moreover, the vast majority of the associated algorithms are limited to 2D images and are often designed with specific applications in mind [10], [18].…”