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Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

1,2, 1,2, 1,*, 1 and 1
1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(9), 1091; https://doi.org/10.3390/rs11091091
Received: 10 April 2019 / Revised: 2 May 2019 / Accepted: 4 May 2019 / Published: 7 May 2019
(This article belongs to the Special Issue SAR in Big Data Era)
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Abstract

With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method. View Full-Text
Keywords: weighted function; color difference image; urban building change detection; synthetic aperture radar (SAR); residual U-Net weighted function; color difference image; urban building change detection; synthetic aperture radar (SAR); residual U-Net
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, L.; Wang, C.; Zhang, H.; Zhang, B.; Wu, F. Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network. Remote Sens. 2019, 11, 1091.

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