Target detection using hyperspectral images is useful for Maritime Domain Awareness (MDA). For future application to MDA, in the previous study, targets on the sea was photographed with a hyperspectral camera mounted on a helicopter to demonstrate a target detection using a Reed-Xiaoli detector (RXD). Although the demonstration turned out to be successful, for there were many erroneous detections due to white waves, improvement of the detection accuracy was desired. In this study, pixels classified as white waves by random forest, which is a supervised machine learning method, were removed from pixels which were regarded as anomaly by RXD.As a result, 76% white waves were successfully removed. This study show that white wave removal is possible by machine learning. This will improve the detection accuracy of foreign matter on the ocean.
Hyperspectral Images are now used in the field of agriculture, cosmetics, and space exploring. Behind this fact, there is a result of efforts to contrive miniaturization and decrease in costs. This paper describes low-cost and small Hyperspectral Camera (HSC) under development and a method of utilizing it. Real Time Detection System for MDA is that government agencies put those cameras in small satellites and use them for MDA (Maritime Domain Awareness). We assume early detection of unidentified floating objects to find out disguised fishing ships and submarines.