Paper
28 January 2025 Building extraction based on multifeature iterative method from remote sensing images
Haoxuan Ma, Yongchuang Wu, Hui Yang, Yanlan Wu
Author Affiliations +
Proceedings Volume 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024); 135062B (2025) https://doi.org/10.1117/12.3057503
Event: Sixth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2024), 2024, Qingdao, China
Abstract
Building extraction methods based on deep learning have technical characteristics such as high extraction accuracy and fast processing speed. However, when using high-resolution remote sensing images, traditional methods often struggle to extract occluded buildings. To address this problem, this paper introduces the Multi-Feature Iterative Model (MFIM), a multi-scale feature iterative model that extracts features at different scales and fuses them in a compressed manner to obtain a structure that preserves global features without losing local details. In order to evaluate the performance of the method, we conducted a comparative analysis on the WHU dataset, using U-Net, DeepLab V3+, and HR-Net as benchmark models. The experimental results indicate that the MFIM method achieves IoU, F1, Recall, and Precision scores of 89.85%, 94.74%, 94.86%, and 94.81%, respectively. This method significantly improves the accuracy of building extraction, and its effectiveness in complex scenes makes it valuable for building extraction tasks.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Haoxuan Ma, Yongchuang Wu, Hui Yang, and Yanlan Wu "Building extraction based on multifeature iterative method from remote sensing images", Proc. SPIE 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), 135062B (28 January 2025); https://doi.org/10.1117/12.3057503
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