Extraction of Building from Remote Sensing Imagery Base on Multi-Attention L-CAFSFM

The extracted building information can be widely applied in urban planning, land resource management, and other related fields. This paper proposes a novel method for building extraction, which aims to improve the accuracy of the extraction process. The method combines a bi-directional feature pyramid with a location-channel attention feature serial fusion module (L-CAFSFM). By using the ResNeXt101 network, more precise and abundant building features are extracted. The L-CAFSFM combines and calculates the adjacent two-level feature maps, and the iteration process from high-level to low-level and from low-level to high-level enhances the feature extraction ability of the model at different scales and levels. We use the DenseCRF algorithm to refine the correlation between pixels. The performance of our method is evaluated on the Wuhan University building dataset (WHU), and the experimental results show that the precision, F-score, recall rate, and IoU of our method are 94.94%, 94.32%, 93.70%, and 89.25%, respectively. Compared with the baseline network, our method achieves a more accurate performance in extracting buildings from high-resolution images. The proposed method can be widely applied in urban planning, land resource management, and other related fields.