Monitoring of marine debris on shorelines using machine-learning models with high-resolution satellite

Tae-Ho Kim; Yeongbin Park

doi:10.1117/12.2663437

12 June 2023 Monitoring of marine debris on shorelines using machine-learning models with high-resolution satellite

Tae-Ho Kim, Yeongbin Park

Proceedings Volume 12543, Ocean Sensing and Monitoring XV; 125430R (2023) https://doi.org/10.1117/12.2663437
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Recently, with the development of high-resolution remote sensing technology and artificial intelligence-based image decoding capability, several detection methods have been studied for shore debris. This study used pan-sharpened KOMPSAT-3A images (spatial resolution: 0.55 m), and the atmospheric correction was performed using the COST model. In order to obtain input data corresponding to Styrofoam, 12 pieces with a size of 0.9 to 3.6 m were installed on the sand, vegetation, and rock, respectively, and 96 pixels were selected through a random sampling method. The classification was performed on four regions of interest. Styrofoam-classified pixels were displayed on the satellite image. As a result of the SVM Linear model, the accuracy was 0.68%, which was very high compared to other models. It was found that the calculated area was underestimated by about 186m2 compared to the drone, which causes microscopic coastal debris due to the relatively low satellite resolution.

Conference Presentation

Citation Download Citation

Tae-Ho Kim and Yeongbin Park "Monitoring of marine debris on shorelines using machine-learning models with high-resolution satellite", Proc. SPIE 12543, Ocean Sensing and Monitoring XV, 125430R (12 June 2023); https://doi.org/10.1117/12.2663437

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