Aerial hyperspectral remote sensing detection for maritime search and surveillance of floating small objects

Abstract

Over the past decades, maritime accidents have been increasing due to the rise in maritime transportation and ship traffic. While detecting accident-prone vessels is crucial, it is equally important to identify individuals in distress and small floating objects. Real -time monitoring and wide-area high-resolution observations enabled by aerial remote sensing have proven effective in maritime detec-tion. In this study, we developed a technology for detecting small objects by conducting two aerial experiments targeting various objects, including ships, mannequins (human-shaped objects), and maritime safety equipment floating in coastal areas, thereby acquiring hyper-spectral image data. By utilizing the hyperspectral data, we detected the pixels corresponding to the edges of ships and employed an ellipse fitting approach to identify the vessels, achieving a length error of 0.44 m. Additionally, we detected small floating objects based on a spectral database using spectral matching. The N-finder algorithm (N-FINDR) spectral unmixing technique was applied to detect lifebuoys, buoyant apparatus, and mannequins, resulting in relatively small length errors ranging from 0.08 to 0.17 m. As satellite hyper-spectral sensors continue to advance significantly, it is expected that this study will contribute to future research in the field of detecting small objects and maritime surveillance. & COPY; 2023 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).