An Energy-Based SAR Image Segmentation Method with Weighted Feature

Yu Wang,Guoqing Zhou,Haotian You

doi:10.3390/rs11101169

Yu Wang, Guoqing Zhou + Show 1 more

Open Access

https://doi.org/10.3390/rs11101169

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Journal: Remote Sensing	Publication Date: May 16, 2019
Citations: 7	License type: CC BY 4.0

Affiliation: Guilin University of Technology

Abstract

To extract more structural features, which can contribute to segment a synthetic aperture radar (SAR) image accurately, and explore their roles in the segmentation procedure, this paper presents an energy-based SAR image segmentation method with weighted features. To precisely segment a SAR image, multiple structural features are incorporated into a block- and energy-based segmentation model in weighted way. In this paper, the multiple features of a pixel, involving spectral feature obtained from original SAR image, texture and boundary features extracted by a curvelet transform, form a feature vector. All the pixels’ feature vectors form a feature set of a SAR image. To automatically determine the roles of the multiple features in the segmentation procedure, weight variables are assigned to them. All the weight variables form a weight set. Then the image domain is partitioned into a set of blocks by regular tessellation. Afterwards, an energy function and a non-constrained Gibbs probability distribution are used to combine the feature and weight sets to build a block-based energy segmentation model with feature weighted on the partitioned image domain. Further, a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm is designed to simulate from the segmentation model. In the RJMCMC algorithm, three move types were designed according to the segmentation model. Finally, the proposed method was tested on the SAR images, and the quantitative and qualitative results demonstrated its effectiveness.

Highlights

Image segmentation is a hot topic in the date processing of a synthetic aperture radar (SAR) image [1,2]
To extract more structural features that can contribute to segmenting accurately and explore the roles of features in the segmentation procedure, this paper presents a feature weighted SAR image segmentation method based on the energy function
To extract more structural features that can contribute to segmenting a SAR image accurately, and explore the roles of the features in the segmentation procedure, this paper proposed a block- and energy-based segmentation method with feature weighted for the SAR image

Summary

Introduction

Image segmentation is a hot topic in the date processing of a synthetic aperture radar (SAR) image [1,2]. In the traditional image segmentation method, homogeneous regions are segmented according to spectral features of pixels [3]. Compared with the middle and low resolution SAR images, the details of high resolution SAR images becomes clearer, but the differences between homogeneous regions becomes larger, and the differences between heterogeneous regions becomes smaller simultaneously [4]. These causes the traditional segmentation method to not reach the segmentation accuracy of a high resolution SAR image [5]. High resolution SAR image segmentation becomes a difficult and hot topic. To segment a high resolution SAR image well, multiple structural features can be considered in the segmentation algorithm [7,8,9]

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Conclusion