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

• Published in: Sustainability 2021-09, Vol.13 (17), p.9568
• Main Authors: Ansari, Emaad, Akhtar, Mohammad Nishat, Abdullah, Mohamad Nazir, Othman, Wan Amir Fuad Wajdi, Bakar, Elmi Abu, Hawary, Ahmad Faizul, Alhady, Syed Sahal Nazli
• Format: Article
• Language: English
• Subjects: Algorithms; Computer techniques; Disasters; Feature recognition; Flood control; Flood management; Flood mapping; Floods; Government agencies; Image processing; Monitoring; Motion perception; National security; Neural networks; River basins; River ecology; Rivers; Satellite imagery; Satellites; Surface flow; Sustainability; Unmanned aerial vehicles; Urban agriculture; Urban areas; Vegetation; Vegetation growth
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su13179568

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.