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http://dx.doi.org/10.7780/kjrs.2022.38.6.1.12

Development of Marine Debris Monitoring Methods Using Satellite and Drone Images  

Kim, Heung-Min (Research Institute, IREMTECH Co. Ltd.)
Bak, Suho (Research Institute, IREMTECH Co. Ltd.)
Han, Jeong-ik (Research Institute, IREMTECH Co. Ltd.)
Ye, Geon Hui (Research Institute, IREMTECH Co. Ltd.)
Jang, Seon Woong (IREMTECH Co. Ltd.)
Publication Information
Korean Journal of Remote Sensing / v.38, no.6_1, 2022 , pp. 1109-1124 More about this Journal
Abstract
This study proposes a marine debris monitoring methods using satellite and drone multispectral images. A multi-layer perceptron (MLP) model was applied to detect marine debris using Sentinel-2 satellite image. And for the detection of marine debris using drone multispectral images, performance evaluation and comparison of U-Net, DeepLabv3+ (ResNet50) and DeepLabv3+ (Inceptionv3) among deep learning models were performed (mIoU 0.68). As a result of marine debris detection using satellite image, the F1-Score was 0.97. Marine debris detection using drone multispectral images was performed on vegetative debris and plastics. As a result of detection, when DeepLabv3+ (Inceptionv3) was used, the most model accuracy, mean intersection over union (mIoU), was 0.68. Vegetative debris showed an F1-Score of 0.93 and IoU of 0.86, while plastics showed low performance with an F1-Score of 0.5 and IoU of 0.33. However, the F1-Score of the spectral index applied to generate plastic mask images was 0.81, which was higher than the plastics detection performance of DeepLabv3+ (Inceptionv3), and it was confirmed that plastics monitoring using the spectral index was possible. The marine debris monitoring technique proposed in this study can be used to establish a plan for marine debris collection and treatment as well as to provide quantitative data on marine debris generation.
Keywords
Sentinel-2; Drone; Multispectral image; Deep learning; Marine debris;
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Times Cited By KSCI : 6  (Citation Analysis)
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