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Instant water body variation detection via analysis on remote sensing imagery

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Abstract

Water resource is one of the most valuable natural resources for human beings, which requires to be monitored for careful protection. Inspired by a significant power of machine learning methods, researchers have successfully developed many applications to automatically perform identification on the water body via analyzing remote sensing images. Since a similar category of ground objects could show a large difference in spectral representation, researchers try to propose distinctive and effective features to offer redundant information for category classification. Moreover, large amount of high-resolution remote sensing images require analyzing algorithms to be parallel processed for instant feedback. Based on these requirements, we propose a novel water body variation detection via analysis on remote sensing images. Specifically, the proposed method firstly perform pixel-level classification to locate abnormal changes with thoughts of visual word patterns. Afterwards, the proposed method proposes block division method to construct parallel running version with Mapreduce structure. With high representational and parallel running abilities, the proposed method is capable to accurately detect variation areas on remote sensing images with instant feedback. Experiments on several self-collected datasets show the proposed method has achieved better efficiencies than comparative studies.

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Acknowledgements

Funding This work was supported by National Key R\&D Program of China under Grant 2018YFC0407901, the Fundamental Research Funds for the Central Universities under Grant B200202177, and the National Natural Science Foundation of China under Grant 61702160.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, Nanjing, China

    Yirui Wu, Pengfei Han & Zhan Zheng

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  1. Yirui Wu
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  2. Pengfei Han
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  3. Zhan Zheng
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Correspondence to Yirui Wu.

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Wu, Y., Han, P. & Zheng, Z. Instant water body variation detection via analysis on remote sensing imagery. J Real-Time Image Proc 18, 1577–1590 (2021). https://doi.org/10.1007/s11554-020-01062-y

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