Convolutional block attention module U-Net: a method to improve attention mechanism and U-Net for remote sensing images

Building extraction from high-resolution remote sensing images is the foundation of many fields, but the existing algorithms cannot extract building details from remote sensing images well because of complex background conditions and the occlusion of interfering objects. To solve the problems of low accuracy and incomplete boundary of traditional building extraction methods, ProCBAM, a parallel attention mechanism based on U-Net network, is adopted and added to the feature transmission step of U-Net. Through the representation of spatial dimension image feature map and channel dimension optimized feature map, we can learn more detailed image information and reduce the error of image recognition. Experiments are carried out on the Massachusetts building dataset, Wuhan University dataset, and IND.v2 dataset, and the experimental results show the effectiveness of this method in building extraction from remote sensing images. (c) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)