Attention Residual U-Net for Building Segmentation in Aerial Images
Chaohui Li, Yingjian Liu, Haoyu Yin, Yue Li, Qingxiang Guo, Limin Zhang, Pengting Du
Abstract
Semantic segmentation of aerial images plays an important role in urban area monitoring. But the diversity of buildings makes segmentation a hard task. To detect buildings from aerial images more precisely, this paper proposes a pixel-level segmentation method, named Attention Residual U-net (ARU-net). ARU-net adds two major part into the framework of U-net, i.e. attention path and residual connection, focusing on feature reuse. Attention path utilizes attention mechanism to capture spatial feature details. Residual connection implies the semantic information flow through a 1×1 convolution similar to the residual form. ARU-net can be trained end-to-end. Experiments are conducted to evaluate the effectiveness of the proposed model on the Inria Aerial Image Labeling Dataset. Results indicate that ARU-net outperforms other baselines with an accuracy of 93.84% and intersection over union (IoU) of 60.90%.