CSAM: A Channel and Spatial Attention Mechanism for Impervious Surface Extraction in Difficult Areas

Impervious surface extraction from remote sensing images has become a promising technology to measure the urban ecological environment and monitor human activity. However, due to the complex characteristics of impervious landscapes, most researches on impervious surface extraction hardly identify the scattered and small objects especially in difficult areas, which severely affect the accuracy of mapping impervious surface. In this work, we propose a channel and spatial attention mechanism (CSAM) to extract impervious surface in difficult areas, which includes a channel attention module to learn the relationship in the multi-channel remote sensing images and a spatial attention module to capture the features of the inconspicuous objects. Experiments with the Sentinel-2 dataset in South Africa demonstrate that CSAM can outperform the state-of-the-art methods.