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Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia

Author

Listed:
  • Emaad Ansari

    (School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Malaysia)

  • Mohammad Nishat Akhtar

    (School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Malaysia)

  • Mohamad Nazir Abdullah

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia)

  • Wan Amir Fuad Wajdi Othman

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia)

  • Elmi Abu Bakar

    (School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Malaysia)

  • Ahmad Faizul Hawary

    (School of Aerospace Engineering, Universiti Sains Malaysia, Engineering Campus, Nibong Tebal 14300, Malaysia)

  • Syed Sahal Nazli Alhady

    (School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Malaysia)

Abstract

The impact of floods is the most severe among the natural calamities occurring in Malaysia. The knock of floods is consistent and annually forces thousands of Malaysians to relocate. The lack of information from the Ministry of Environment and Water, Malaysia is the foremost obstacle in upgrading the flood mapping. With the expeditious evolution of computer techniques, processing of satellite and unmanned aerial vehicle (UAV) images for river hydromorphological feature detection and flood management have gathered pace in the last two decades. Different image processing algorithms—structure from motion (SfM), multi-view stereo (MVS), gradient vector flow (GVF) snake algorithm, etc.—and artificial neural networks are implemented for the monitoring and classification of river features. This paper presents the application of the k-means algorithm along with image thresholding to quantify variation in river surface flow areas and vegetation growth along Kerian River, Malaysia. The river characteristic recognition directly or indirectly assists in studying river behavior and flood monitoring. Dice similarity coefficient and Jaccard index are numerated between thresholded images that are clustered using the k-means algorithm and manually segmented images. Based on quantitative evaluation, a dice similarity coefficient and Jaccard index of up to 97.86% and 94.36% were yielded for flow area and vegetation calculation. Thus, the present technique is functional in evaluating river characteristics with reduced errors. With minimum errors, the present technique can be utilized for quantifying agricultural areas and urban areas around the river basin.

Suggested Citation

  • Emaad Ansari & Mohammad Nishat Akhtar & Mohamad Nazir Abdullah & Wan Amir Fuad Wajdi Othman & Elmi Abu Bakar & Ahmad Faizul Hawary & Syed Sahal Nazli Alhady, 2021. "Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:17:p:9568-:d:621718
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    References listed on IDEAS

    as
    1. Takahiro Oga & Ryosuke Harakawa & Sayaka Minewaki & Yo Umeki & Yoko Matsuda & Masahiro Iwahashi, 2020. "River state classification combining patch-based processing and CNN," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-14, December.
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    Cited by:

    1. Mohammad Nishat Akhtar & Mohd Talha Anees & Emaad Ansari & Jazmina Binti Ja’afar & Mohammed Danish & Elmi Abu Bakar, 2022. "Baseline Assessment of Heavy Metal Pollution during COVID-19 near River Mouth of Kerian River, Malaysia," Sustainability, MDPI, vol. 14(7), pages 1-16, March.

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