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http://dx.doi.org/10.7837/kosomes.2022.28.7.1120

Hyperspectral Image Analysis Technology Based on Machine Learning for Marine Object Detection  

Sangwoo Oh (Ocean and Maritime Digital Technology Research Division, KRISO)
Dongmin Seo (Ocean and Maritime Digital Technology Research Division, KRISO)
Publication Information
Journal of the Korean Society of Marine Environment & Safety / v.28, no.7, 2022 , pp. 1120-1128 More about this Journal
Abstract
In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km2 within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.
Keywords
Maritime object detection; Hyperspectral; Artificial intelligence; Machine learning; Maritime search; Ship detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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