Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR

Kaiyu Zhang,Xiaolei Lv,Xikai Fu,Jili Yuan

doi:10.3390/rs13030471

Abstract

Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs.

Highlights

Change detection is a process of automatically analyzing and identifying the variation of Earth’s surface objects based on multitemporal remote sensing images acquired in the same region at different times [1,2]
For binary-date change detection, i.e., N = 2, R = | x2 − x1 |, which is equivalent to the classical difference detector
The change frequency map (CFM) and part of change moment maps (CMMs) of the two datasets are illustrated in Figures 6 and 7

Summary

Introduction

Change detection is a process of automatically analyzing and identifying the variation of Earth’s surface objects based on multitemporal remote sensing images acquired in the same region at different times [1,2]. As a significant application of remote sensing image, change detection analysis provides an effective technological significance for land use and land cover monitoring [3,4], urban planning and management [5,6], natural disaster assessment and monitoring [7,8,9], etc. Optical remote sensing systems require good sunlight and weather conditions to acquire high-quality optical images. Synthetic aperture radar (SAR) can acquire data all day without relying on a light source, which is suitable for some emergencies such as natural disaster survey. According to the number of images, change detection can be divided into binary-date change detection and multi-temporal change detection.

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Conclusion