Building extraction aims to separate building pixels from remote sensing images, which plays a crucial role in applications such as urban planning and urban dynamic monitoring. However, building extraction generally faces challenges, such as void, false positives, and false negatives. Given this, this paper proposed a densely nested network (DN-Net). The sub-networks in the DN-Net were integrated with the enhanced residual convolutional module (ERCM) to extract rough contours of buildings from remote sensing images. Furthermore, to accurately locate the buildings, a coordinate attention module (CAM) was incorporated, effectively avoiding false positives. To deal with the holes during building extraction, a cascade convolutional module (CCM) was used, allowing the extraction of richer details with convolution kernels of various sizes, thereby ensuring accurate building extraction. The DN-Net was tested with the WHU datasets to assess its accuracy. The results showed that the DN-Net exhibited an intersection over union (IoU) of 89.20% and a F1 score of 94.29% on the validation set and 89.85% and 94.65%, respectively, on the test set. The results confirm that the DN-Net can significantly improve the building extraction accuracy, with more complete and detailed boundaries of buildings being extracted, demonstrating an outstanding ability to extract buildings of varying sizes.
刘毅, 刘涛, 高天迎, 李国燕. DN-Net:密集嵌套网络的遥感建筑物提取[J]. 自然资源遥感, 2025, 37(6): 77-87.
LIU Yi, LIU Tao, GAO Tianying, LI Guoyan. DN-NET: A densely nested network for building extraction from remote sensing images. Remote Sensing for Natural Resources, 2025, 37(6): 77-87.
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2025, Vol. 37 
), 刘涛, 高天迎(
