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21 June 2024 Deep learning-based active contour model for remote sensing building image segmentation
Tingting Geng, Zhihuang He, Linsong Hu, Zifeng Wang, Le Zou
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Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131670H (2024) https://doi.org/10.1117/12.3029641
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China
Abstract
Remote sensing building image segmentation faces complex building boundary and shape challenges due to the underutilisation of local features and global geometric information, as well as the dependence on initial contours. To overcome these problems, this paper proposes an end-to-end modelling framework called ACM-ResNet, which integrates the levelset Active Contour Model (ACM) and the ResNet deep learning network. ACM-ResNet utilises evolving level-set functions to progressively optimise the accuracy and smoothness of the contours, thus effectively addressing complex building boundary and shape challenges. Most importantly, ACM-ResNet no longer relies on manually labelling or setting the initial contour, but adaptively adjusts the contour position and shape starting from the predicted initial contour, improving the automation and robustness of the segmentation process. We evaluate our segmentation model on the Vaihingen and Bing Huts datasets, and the experimental results show that our method outperforms competing methods in terms of performance.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Tingting Geng, Zhihuang He, Linsong Hu, Zifeng Wang, and Le Zou "Deep learning-based active contour model for remote sensing building image segmentation", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131670H (21 June 2024); https://doi.org/10.1117/12.3029641
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KEYWORDS
Image segmentation
Remote sensing
Contour modeling
Education and training
Deep learning
Image processing
Visualization
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