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Improved U-Net model for remote sensing image classification method based on distributed storage

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Abstract

Aiming at the low efficiency of traditional methods for the management and classification of massive remote sensing image data, a mass remote sensing image classification method based on distributed storage is proposed. The aim is to obtain near real-time image classification in mobile devices or internet applications. In this paper, we designed two levels of an image processing structure. A distributed file system is taken as the underlying storage architecture to efficiently manage and query massive remote sensing images. The upper layer uses a GPU server to train the remote sensing image classification model to improve the classification accuracy. To improve the classification accuracy, we add two parameters to adjust the data of the current layer in U-Net. The experimental results show that the proposed method based on distributed storage has a high degree of scalability, and it has a short processing time while maintaining a high classification accuracy for remote sensing images.

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Acknowledgements

The work described in this paper is supported by National Natural Science Foundation of China (31770768), Heilongjiang Province Applied Technology Research and Development Program Major Project (GA18B301) and China State Forestry Administration Forestry Industry Public Welfare Project (201504307).

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Authors and Affiliations

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang, China

    Weipeng Jing, Mingwei Zhang & Dongxue Tian

  2. Key Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration, Harbin, Heilongjiang, China

    Weipeng Jing, Mingwei Zhang & Dongxue Tian

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  1. Weipeng Jing
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  2. Mingwei Zhang
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  3. Dongxue Tian
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Correspondence to Mingwei Zhang.

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Jing, W., Zhang, M. & Tian, D. Improved U-Net model for remote sensing image classification method based on distributed storage. J Real-Time Image Proc 18, 1607–1619 (2021). https://doi.org/10.1007/s11554-020-01028-0

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