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Automatic Detection of Waterbodies from Satellite Images Using DeepLabV3+

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Mining Intelligence and Knowledge Exploration (MIKE 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13924))

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Abstract

In recent times, it has become increasingly popular to examine a wide range of environmental and earth data using remote sensing schemes supported by satellite images (SI). Due to the complex nature of spatial, spectral, and temporal characteristics of SI, it is quite difficult for automatic analysis to be performed as it requires specially designed algorithms. As part of the research proposal, an enhanced deep-learning scheme will be implemented in order to extract the waterbodies from the chosen SIs. The phases involved in this scheme includes; (i) the collection and resizing of images, (ii) Shannon's Entropy preprocessing, (iii) DeepLabV3+ extraction of waterbodies, (iv) the comparison of extracted sections with ground truth (GT) and the calculation of performance metrics, and (v) validation of the effectiveness of the implemented scheme. In the proposed work, the proposed multi-thresholding approach is combined with DeepLabV3+ in order to get the waterbodies to be extracted from the chosen test images. DeepLabV3 is demonstrated to have excellent segmentation performance when it is compared to UNet and SegNet. The experimental results of this scheme indicate that DeepLabV3+ results in higher Jaccard value (>89%), Dice value (>93%), and segmentation accuracy value (>97%) when compared to other pretrained segmentation schemes.

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Authors and Affiliations

  1. Department of Applied Data Science, Noroff University College, 4612, Kristiansand, Norway

    Seifedine Kadry & Sahar Yassine

  2. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, United Arab Emirates

    Mohammed Azmi Al-Betar

  3. Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, 602105, India

    Ramya Mohan, Rama Arunmozhi & Venkatesan Rajinikanth

Authors
  1. Seifedine Kadry
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  2. Mohammed Azmi Al-Betar
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  3. Sahar Yassine
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  4. Ramya Mohan
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  5. Rama Arunmozhi
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  6. Venkatesan Rajinikanth
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Corresponding author

Correspondence to Seifedine Kadry .

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Editors and Affiliations

  1. Noroff University College, Kristiansand, Norway

    Seifedine Kadry

  2. Indian Institute of Information Technology, Sri City, India

    Rajendra Prasath

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Kadry, S., Al-Betar, M.A., Yassine, S., Mohan, R., Arunmozhi, R., Rajinikanth, V. (2023). Automatic Detection of Waterbodies from Satellite Images Using DeepLabV3+. In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_8

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  • DOI: https://doi.org/10.1007/978-3-031-44084-7_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44083-0

  • Online ISBN: 978-3-031-44084-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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