A Novel Impervious Surface Extraction Method Based on Transformer

The amount of impervious surface is an important indicator to measure the degree of urbanization and the urban ecological environment. However, the objects in the low-density impervious surface areas are small and scattered, which are easily confused with the background. Therefore, the extraction of the small and scattered impervious surfaces is still challenging. In this study, we propose a dual-branch network combing transformer and CNN with attention mechanism. In this model, transformer branch is first used to extract impervious surface to capture long-distance and large-scale dependencies. In addition, another UNet branch embedded the coordinate attention mechanism can capture detailed information and meanwhile reduce information redundancy. Experiments show that our proposed method performs better than the traditional CNN methods.