CSA-UNet: Channel-Spatial Attention-Based Encoder–Decoder Network for Rural Blue-Roofed Building Extraction From UAV Imagery

Abstract

Building extraction is a critical part of remote sensing (RS) image interpretation, and it is a popular research topic in the RS community. However, building extraction from RS images is a difficult task due to its various shape, size, and complex scene. The extracted feature of existing deep learning methods is lack of discrimination, resulting in incomplete buildings and irregular boundaries. Most studies are mainly concentrated on urban areas, ignoring illegal blue-roofed building extraction in rural areas. To address the above-mentioned problems, a channel-spatial attention-based encoder-decoder network (CSA-UNet) is proposed for rural blue-roofed building extraction tasks from RS images. To extract the key areas of buildings, the CSA-UNet employed channel-spatial attention to the fused features of encoder and decoder for achieving discriminative and attentive features. At the same time, considering the problem of false negative predictions, a joint loss function is designed by giving weight to positive samples to alleviate this problem and optimize the CSA-UNet model. Furthermore, blue-roofed buildings are a special type of illegal building, so we take blue-roofed buildings as an example to carry out related research. And a blue roof dataset termed UAVBlue is built through unmanned aerial vehicle (UAV). Experimental results exhibit that the CSA-UNet is better than some state-of-the-art (SOTA) methods.