A Comparative Study of U-Net Architectures for Change Detection in Satellite Images
It addresses the gap in applying U-Net architectures to remote sensing change detection, offering guidance for researchers and practitioners in this domain, though it is incremental as it reviews and compares existing methods.
This paper conducted a comparative analysis of 18 U-Net variations for change detection in satellite images, evaluating their benefits and drawbacks to provide insights for selecting architectures in remote sensing applications.
Remote sensing change detection is essential for monitoring the everchanging landscapes of the Earth. The U-Net architecture has gained popularity for its capability to capture spatial information and perform pixel-wise classification. However, their application in the Remote sensing field remains largely unexplored. Therefore, this paper fill the gap by conducting a comprehensive analysis of 34 papers. This study conducts a comparison and analysis of 18 different U-Net variations, assessing their potential for detecting changes in remote sensing. We evaluate both benefits along with drawbacks of each variation within the framework of this particular application. We emphasize variations that are explicitly built for change detection, such as Siamese Swin-U-Net, which utilizes a Siamese architecture. The analysis highlights the significance of aspects such as managing data from different time periods and collecting relationships over a long distance to enhance the precision of change detection. This study provides valuable insights for researchers and practitioners that choose U-Net versions for remote sensing change detection tasks.