Kim KL, Woo HJ, Jou HT, Jung HC, Lee SK, Ryu JH. Surface sediment classification using a deep learning model and unmanned aerial vehicle data of tidal flats.
MARINE POLLUTION BULLETIN 2024;
198:115823. [PMID:
38039578 DOI:
10.1016/j.marpolbul.2023.115823]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 11/11/2023] [Accepted: 11/17/2023] [Indexed: 12/03/2023]
Abstract
This study proposes a deep learning model, U-Net, to improve surface sediment classification using high-resolution unmanned aerial vehicle (UAV) images. We constructed training datasets with UAV images and corresponding labeling data acquired from three field surveys on the Hwangdo tidal flat. The labeling data indicated the distribution of surface sediment types. We compared the performance of the U-Net model trained in various implementation environments, such as surface sediment criteria, input datasets, and classification models. The U-Net trained with five class criteria-derived from previous classification criteria-yielded valid results (overall accuracy:65.6 %). The most accurate results were acquired from trained U-Net with all input datasets; in particular, the tidal channel density caused a significant increase in accuracy. The accuracy of the U-Net was approximately 20 % higher than that of other classification models. These results demonstrate that surface sediment classification using UAV images and the U-Net model is effective.
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