D-CDA:A Denoise And Change Detection Approach For Flood disaster location From SAR Images

Flood disasters are among the most devastating natural disasters worldwide. The occurrence of such disasters is often accompanied by strong precipitation and other weather factors, making it more difficult to identify the affected areas. Moreover, Synthetic Aperture Radar (SAR) technology can capture images in 24-hour window and penetrate through clouds and fog. The change detection technology, based on SAR images, is generally utilized to locate disaster-stricken areas by analyzing the differences between pre- and post-disaster images. However, this method mainly faces two challenges: the presence of speckle noise reduces the accuracy of the difference detection and the lack of a suitable SAR dataset for flood disaster change detection. Therefore, this research proposes a novel two-stage approach for locating the flood disaster area, named Denoising-Change Detection Approach (D-CDA). The first stage consists of a nine-layer denoising network with an encoder-decoder structure, called SAR Denoising Network (SDNet). It utilizes a multi-residual block and parallel convolutional block attention module to extract features during the encoding process to suppress the noise component. In the second stage, a novel convolution neural network is proposed to detect the changes between bitemporal SAR images, namely Coordinate Attention Fused Network (CAFNet), which combines the Siamese network and UNet++ as the backbone and fuses multi-coordinate attention modules to enhance the change features. Moreover, a change detection dataset (ZhengZhou Flood - ZZF-dataset) is constructed using Sentinel-1 SAR images based on the flood disaster in Zhengzhou of China in 2021. The simulations verify the effectiveness of the proposed method.