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A lightweight building change detection network with coordinate attention and multiscale fusion

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Abstract

Real-time and rapid detection of building changes can play an important role in natural resource management. In practical change detection, many existing change detection methods need to be further improved, for example, in terms of reducing the computational cost of modeling and improving model accuracy. In this article, we propose a lightweight Siamese multiscale fusion network for building change detection. The encoder uses coordinate attention mechanism enhancement feature extraction, while the decoder uses multi-scale fusion to reconstruct the change region. In the network structure, 3 × 1, 1 × 3 and 1 × 1 convolution is widely used to replace 3 × 3 to reduce network model parameters. Finally, we use a hybrid loss function to alleviate the sample imbalance problem and a deep supervision strategy to fully utilize network hidden layer information. In comparison to other models, the model proposed in this paper achieves the best scores of 90.25%, 82.23% and 90.39%, 82.47% for its recall and IOU metrics on the LEVIR-CD and WHU-CD datasets, respectively. Experimental results show that the network model in this paper performs well in change detection.

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No datasets were generated or analysed during the current study.

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Funding

This research was funded by National Natural Science Foundation of China (grant NO. 42361054) Yunnan Fundamental Research Projects (grant NO.202201AT070164), Hunan Provincial Natural Science Foundation of China (grant NO. 2023JJ60561) and "Xingdian" talent support program project.

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Authors and Affiliations

  1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, People’s Republic of China

    Weipeng Le & Liang Huang

  2. Key Laboratory of Plateau Remote Sensing, Yunnan Provincial Department of Education, Kunming, People’s Republic of China

    Liang Huang

Authors
  1. Weipeng Le
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  2. Liang Huang
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Contributions

W.L.: algorithm proposed and testing, manuscript writing, and research conceptualization. L.H.: funding acquisition, directing, and manuscript writing.

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Correspondence to Liang Huang.

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Communicated by H. Babaie

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Le, W., Huang, L. A lightweight building change detection network with coordinate attention and multiscale fusion. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01315-5

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