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Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning

  • S.I: Cognitive-inspired Computing and Applications
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Abstract

Extracting and recognizing buildings from high-resolution remote sensing images faces many problems due to the complexity of the buildings on the surface. The purpose is to improve the recognition and extraction capabilities of remote sensing satellite images. The Gao Fen-2 (GF-2) high-resolution remote sensing satellite is taken as the research object. The deep convolutional neural network (CNN) serves as the core of image feature extraction, and PCA (principal component analysis) is adopted to reduce the dimensionality of the data. A correction neural network model, that is, boundary regulated network (BR-Net) is proposed. The features of remote sensing images are extracted through convolution, pooling, and classification. Different data collection models are utilized for comparative analysis to verify the performance of the proposed model. Results demonstrate that when using CNN to recognize remote sensing images, the recognition accuracy is much higher than that of traditional image recognition models, which can reach 95.3%. Compared with the newly researched models, the performance is improved by 15%, and the recognition speed is increased by 20%. When extracting buildings with higher accuracy, the proposed model can also ensure clear boundaries, thereby obtaining a complete building image. Therefore, using deep learning technology to identify and extract buildings from high-resolution satellite remote sensing images is of great significance for advancing the deep learning applications in image recognition.

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Acknowledgements

This work was supported by Changsha Municipal Natural Science Foundation(KQ2007084), Research Foundation of Education Bureau of Hunan Province, China (Grant No.19B321) and NSFC (Grant No.61772182). This work was supported by Guangdong philosophy and Social Science Planning Project, Project No.:GD19YYS08, and Guangdong University Youth Innovation Talent Project, Project No.:2020WQNCX001.

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Authors and Affiliations

  1. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, People’s Republic of China

    Yifu Zeng

  2. Hunan Province Key Laboratory of Industrial Internet Technology and Security, Changsha University, Changsha, People’s Republic of China

    Yifu Zeng

  3. School of Design, State Key Laboratory of Subtropical Building Science, Institute of Micro Science And Art of SCUT, South China University Of Technology Guangzhou, Guangdong, 510006, People’s Republic of China

    Yi Guo

  4. Department of Control and Computer Engineering, Polytechnic University of Turin, 10129, Turin, Italy

    Jiayi Li

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  1. Yifu Zeng
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  2. Yi Guo
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Correspondence to Yi Guo.

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Zeng, Y., Guo, Y. & Li, J. Recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning. Neural Comput & Applic 34, 2691–2706 (2022). https://doi.org/10.1007/s00521-021-06027-1

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