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Change Detection of Flood Hazard Areas in Multi-Source Heterogeneous Earth Observation Image Time Series Based on Spatiotemporal Enhancement Strategy

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Artificial Intelligence (CICAI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13604))

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Abstract

Change detection (CD) on multiple remote sensing images has been widely used for monitoring flood changes. In this paper, we innovatively propose a spatiotemporal enhanced CD (STECD) algorithm, which exploits the spatial and temporal dependence of multi-source heterogeneous (MSH) Earth Observation image time series (EOITS). Our STECD algorithm mainly contains two steps, i.e., spatial clustering and temporal enhancement. In the spatial clustering step, we propose a sparse Markov random field (SMRF)-based strategy to iteratively optimize the boundaries of flood areas in accordance with contextually spatial features in each image of MSH EOITS. In the temporal enhancement step, the historical incremental information of flood areas is employed as constraints to effectively reduce the effects of terrain shadows (in synthetic aperture radar (SAR) images) and cloud shadows and topography shadows (in optical images) on spatial clustering results in accordance with the temporal dependence among MSH EOITS. Experiments on real MSH EOITS show that the overall detection accuracy of our proposed STECD algorithm is higher than existing commonly used methods for CD of flood hazard areas.

Supported by National Key R &D Program of China under Grant 2021YFA0715201, National Natural Science Foundation of China under Grants 61790551, 61925106, and 62101303, and Autonomous Research Project of Department of Electronic Engineering at Tsinghua University.

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Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Zhihao Wang, Xueqian Wang & Gang Li

Authors
  1. Zhihao Wang
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  2. Xueqian Wang
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  3. Gang Li
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Corresponding author

Correspondence to Gang Li .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Lu Fang

  2. Xiaomi Inc., Beijing, China

    Daniel Povey

  3. Shanghai Jiao Tong University, Shanghai, China

    Guangtao Zhai

  4. JD Explore Academy, Beijing, China

    Tao Mei

  5. Chinese Academy of Sciences, Beijing, China

    Ruiping Wang

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Wang, Z., Wang, X., Li, G. (2022). Change Detection of Flood Hazard Areas in Multi-Source Heterogeneous Earth Observation Image Time Series Based on Spatiotemporal Enhancement Strategy. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13604. Springer, Cham. https://doi.org/10.1007/978-3-031-20497-5_37

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  • DOI: https://doi.org/10.1007/978-3-031-20497-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20496-8

  • Online ISBN: 978-3-031-20497-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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