Affiliation:
1. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Earth Observation of Hainan Province, Hainan Aerospace Information Research Institute, Sanya 572029, China
Abstract
Building extraction based on remote sensing images has been widely used in many industries. However, state-of-the-art methods produce an incomplete segmentation of buildings owing to unstable multi-scale context aggregation and a lack of consideration of semantic boundaries, ultimately resulting in large uncertainties in predictions at building boundaries. In this study, efficient fine building extraction methods were explored, which demonstrated that the rational use of edge features can significantly improve building recognition performance. Herein, a fine building extraction network based on a multi-scale edge constraint (MEC-Net) was proposed, which integrates the multi-scale feature fusion advantages of UNet++ and fuses edge features with other learnable multi-scale features to achieve the effect of prior constraints. Attention was paid to the alleviation of noise interference in the edge features. At the data level, according to the improvement of copy-paste according to the characteristics of remote sensing imaging, a data augmentation method for buildings (build-building) was proposed, which increased the number and diversity of positive samples by simulating the construction of buildings to increase the generalization of MEC-Net. MEC-Net achieved 91.13%, 81.05% and 74.13% IoU on the WHU, Massachusetts and Inria datasets, and it has a good inference efficiency. The experimental results show that MEC-Net outperforms the state-of-the-art methods, demonstrating its superiority. MEC-Net improves the accuracy of building boundaries by rationally using previous edge features.
Funder
National Key R&D Program of China
Finance Science and Technology Project of Hainan Province
Fujian Provincial Science and Technology Plan Project
Subject
General Earth and Planetary Sciences
Cited by
7 articles.
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