A Novel Method of Unsupervised Change Detection Using Multi-Temporal PolSAR Images

Wensong Liu,Jie Yang,Jinqi Zhao,Le Yang

doi:10.3390/rs9111135

Abstract

The existing unsupervised change detection methods using full-polarimetric synthetic aperture radar (PolSAR) do not use all the polarimetric information, and the results are subject to the influence of noise. In order to solve these problems, a novel automatic and unsupervised change detection approach based on multi-temporal full PolSAR images is presented in this paper. The proposed method integrates the advantages of the test statistic, generalized statistical region merging (GSRM), and generalized Gaussian mixture model (GMM) techniques. It involves three main steps: (1) the difference image (DI) is obtained by the likelihood-ratio parameter based on a test statistic; (2) the GSRM method is applied to the DI; and (3) the DI, after segmentation, is automatically analyzed by the generalized GMM to generate the change detection map. The generalized GMM is derived under a non-Gaussian assumption for modeling the distributions of the changed and unchanged classes, and automatically identifies the optimal number of components. The efficiency of the proposed method is demonstrated with multi-temporal PolSAR images acquired by Radarsat-2 over the city of Wuhan in China. The experimental results show that the overall accuracy of the change detection results is improved and the false alarm rate reduced, when compared with some of the traditional change detection methods.

Highlights

In the field of remote sensing, change detection techniques are applied to recognize changes of the ground by analyzing remote sensing images taken at different times in the same geographical area [1,2]
We use the generalized statistical region merging (GSRM) algorithm to separate the same parts of the difference image (DI), which helps us to choose the threshold by generalized Gaussian mixture model (GMM)
A novel method of automatic and unsupervised change detection using multi-temporal polarimetric synthetic aperture radar (PolSAR) images has been presented in this paper

Summary

Introduction

In the field of remote sensing, change detection techniques are applied to recognize changes of the ground by analyzing remote sensing images taken at different times in the same geographical area [1,2]. Change detection with SAR images has been used in applications such as disaster monitoring [4,5], ecological monitoring [6,7], regulatory policy development [8,9], and environmental impact assessment [10]. Many researchers have focused on change detection using multi-temporal PolSAR images [12,13,14,15]. Based on whether training data are accessed or not, change detection based on PolSAR images can be generally classified into two categories: unsupervised approaches and supervised approaches

Methods

Discussion

Conclusion