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.
Highlights
Urban change detection is an essential remote sensing application that analyzes two or more remote sensing images that have been acquired over the same geographical area at different times to find changes that may have occurred between their acquisition dates [1]
By introducing spatial and intensity information into DIs, we propose a new combination method based on the idea of neighbor-based ratio (NR) difference image [12] and weighting function in reference [14]
According to the confusion matrices, we found that most of the building changes were correctly detected in both areas
Summary
Urban change detection is an essential remote sensing application that analyzes two or more remote sensing images that have been acquired over the same geographical area at different times to find changes that may have occurred between their acquisition dates [1]. As mentioned in reference [3], the procedure of change detection in SAR images can be divided into three steps: (i) Image processing; (ii) difference image (DI) generation; (iii) analysis of DI. The common disadvantage of them was that the optimal window size of the neighborhood was difficult to determine, since there was no reference map or prior knowledge about the image To solve this problem, Zhuang et al [14] employed heterogeneity to adaptively select the spatial homogeneity neighborhood and used the temporal adaptive strategy to determine multi-temporal neighborhood windows. Bovolo et al [1] combined multiscale image information to preserve change details by using wavelet decomposition under log-ratio images It can be concluded from the above-mentioned improvement methods that combining different DIs or using neighborhood information could help to improve change detection performance
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