Multi-satellite Ship Detection using Optical, Hyperspectral, and Microwave SAR Remote Sensing Data in Coastal Regions

Abstract

The necessity of efficient monitoring of the ships at the coastal regions has been increasing over time. Multi-satellite observations make it possible to monitor vessels effectively in the coastal region. This study presents the results ofship detection methodology applied to optical, hyperspectral, and microwave satellite images in the seas around Korean Peninsula. To detect the ships from hyperspectral images with hundreds of spectral channels, spectral matching algorithms are used to investigate similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from backscattering coefficients of Sentinel-1B SAR and ALOS-2 PALSAR2 images. Validation results exhibited that the locations of the satellite-detected vessels showed good agreement with real-time location data within the Sentinel-1B coverage in the Korean coastal region. This study presented the probability of detection values of optical and SAR-based ship detection and discussed potential causes of the errors. This study also suggested a possibility of for real-time operational use of vessel detection from multi-satellite images based on optical, hyperspectral, and SAR remote sensing, particularly at the inaccessible coastal regions off North Korea, for comprehensive coastal management and sustainability.