Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Siamese U-net with Attention Mechanism for Building Change Detection in High-Resolution Remote Sensing Images
Building change detection in high-resolution remote sensing images is very important for illegal building management and urban supervision. Recently, with the development of neural network and the increase of RS data, there are more and more change detection methods based on deep learning. Most of the existing change detection algorithms based on deep differential feature analysis which detect all semantic changes in two-temporal images, not specifically designed for building change detection and unable to give an accurate mask for building changes area. In this paper, we propose a Siamese U-net with attention mechanism for building change detection in high-resolution bi-temporal remote sensing images. By introducing scene-level building segmentation, we improve the boundary integrity and internal compactness of the final changed building. Our method was applied to WHU dataset and have outstanding building change detection results.
Siamese U-net with Attention Mechanism for Building Change Detection in High-Resolution Remote Sensing Images
Building change detection in high-resolution remote sensing images is very important for illegal building management and urban supervision. Recently, with the development of neural network and the increase of RS data, there are more and more change detection methods based on deep learning. Most of the existing change detection algorithms based on deep differential feature analysis which detect all semantic changes in two-temporal images, not specifically designed for building change detection and unable to give an accurate mask for building changes area. In this paper, we propose a Siamese U-net with attention mechanism for building change detection in high-resolution bi-temporal remote sensing images. By introducing scene-level building segmentation, we improve the boundary integrity and internal compactness of the final changed building. Our method was applied to WHU dataset and have outstanding building change detection results.
Siamese U-net with Attention Mechanism for Building Change Detection in High-Resolution Remote Sensing Images
Lect. Notes Electrical Eng.
Jing, Zhongliang (Herausgeber:in) / Strelets, Dmitry (Herausgeber:in) / Song, Yiren (Autor:in) / Jing, Zhongliang (Autor:in) / Li, Minzhe (Autor:in)
International Conference on Aerospace System Science and Engineering ; 2021
Proceedings of the International Conference on Aerospace System Science and Engineering 2021 ; Kapitel: 37 ; 487-503
09.07.2022
17 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Change detection , Deep learning , Fully convolutional Siamese network , Remote sensing image processing Engineering , Aerospace Technology and Astronautics , Communications Engineering, Networks , Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) , Control and Systems Theory
Semi-Supervised Building Detection from High-Resolution Remote Sensing Imagery
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