A Spatial-Temporal Adaptive Neighborhood-Based Ratio Approach for Change Detection in SAR Images

Abstract

The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.

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

“…Therefore, the goal of this paper was to generate a new DI which included an object's class information, which not only better distinguishes changed and unchanged areas, but also divides the urban building change into positive and negative changes. By introducing spatial and intensity information into DIs, we propose a new combination method based on the idea of NR difference image [12] and weighting function in reference [14]. Considering the complexity of urban environment and the small scale of building changes, the construction and classifier of the residual U-Net network are modified to distinguish between different building changes.…”

“…However, 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. This way, the new DI could both suppress the negative influence of noise and preserve edge details.…”

Robust unsupervised small area change detection from SAR imagery using deep learning

“…In the step of DI generation, several methods have been developed to exploit neighbourhood spatial information in a local window, such as MR and NR. In (Zhuang et al, 2018), the authors proposed a spatialtemporal adaptive neighbourhood-based ratio to select optimal window size for solving the shortcoming of fixed-size rectangular window. Further, an adaptive generalized likelihood ratio test (AGLRT) was developed to weaken the geometric degradation of the DI caused by integrating heterogeneous pixels (Zhuang et al, 2020).…”

It is a misunderstanding that log ratio outperforms ratio in change detection of SAR images

“…Instead of ratio operator, log ratio operator was selected to obtain the difference image in the literature (Bovolo & Bruzzone, 2005). It is well known for the researchers who focus on change detection of SAR images that the performance of log ratio is better than ratio (Bovolo & Bruzzone, 2005;Zhuang, Fan, Deng, & Yao, 2018;Zhuang, Fan, Deng, & Yu, 2018). 1 Therefore, log ratio was employed as one of the competitive approaches in many literature focusing on change detection of SAR images (Gong, Cao, & Wu, 2012;Su, Gong, & Sun, 2014;Zhuang, Deng, Fan, & Ma, 2018).…”