CD-TransUNet: A Hybrid Transformer Network for the Change Detection of Urban Buildings Using L-Band SAR Images

Lei Pang, Jinjin Sun (), Yancheng Chi, Yongwen Yang, Fengli Zhang and Lu Zhang
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Lei Pang: School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Jinjin Sun: School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China
Yancheng Chi: Bei Jing Guo Wen Xin Cultural Relics Protection Co., Ltd., Beijing 100029, China
Yongwen Yang: Bei Jing Guo Wen Xin Cultural Relics Protection Co., Ltd., Beijing 100029, China
Fengli Zhang: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Lu Zhang: China Land Surveying and Planning Institute, Beijing 100032, China

Sustainability, 2022, vol. 14, issue 16, 1-18

Abstract: The change detection of urban buildings is currently a hotspot in the research area of remote sensing, which plays a vital role in urban planning, disaster assessments and surface dynamic monitoring. SAR images have unique characteristics compared with traditional optical images, mainly including abundant image information and large data volume. However, the majority of currently used SAR images for the detection of changes in buildings have the problems of missing the detection of small buildings and poor edge segmentation. Therefore, this paper proposes a new approach based on deep learning for changing building detection, which we called CD-TransUNet. It should be noted that CD-TransUNet is an end-to-end encoding–decoding hybrid Transformer model that combines the UNet and Transformer. Additionally, to enhance the precision of feature extraction and to reduce the computational complexity, the CD-TransUNet integrates coordinate attention (CA), atrous spatial pyramid pooling (ASPP) and depthwise separable convolution (DSC). In addition, by sending the differential images to the input layer, the CD-TransUNet can focus more on building changes over a large scale while ignoring the changes in other land types. At last, we verify the effectiveness of the proposed method using a pair of ALOS-2(L-band) acquisitions, and the comparative experimental results obtained from other baseline models show that the precision of the CD-TransUNet is much higher and the Kappa value can reach 0.795. Furthermore, the low missed alarms and the accurate building edge reflect that the proposed method is more appropriate for building changing detection tasks.

Keywords: urban building change detection; UNet; Transformer; lightweight network; L-band SAR image (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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