In recent years, Maritime Domain Awareness (MDA) has become important for national defense in Japan. Target detection using hyperspectral data is useful for MDA. In this study, we found that Correlation Matched Filter (CMF) has a better detection accuracy than Spectral Matched Filter (SMF), both of which are derived from Reed-Xiaoli Detector. CMF doesn't need to calculate the average value of the background spectrum, which is also advantageous in real-time processing. In addition, we could also show that it is possible to improve the detection accuracy by band selection in CMF. This increases the detection accuracy of foreign matter on the ocean.
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.