The Application of Hyperspectral Sensing Data for Seabed Classification in the Coastal Area of Korea

Seabed classification is the important part of current coastal research because it characterizes the seabed and its habitats. Seabed characterization makes the link between the classified area and the physical, geological, chemical or biological properties of seabed. This paper addresses the possibilities of the use of airborne remote sensing with a CASI-1500 hyperspectral sensor to map the coverage and the topography of seabed in the western coastal part of Korea. From April to October in 2012, hyperspectral imagery was acquired at low tide. After radiometric, geometric and atmospheric correction for the raw images, the classification was performed in three steps. Firstly, ten classes of seabed were identified using a supervised spectral angle mapping algorithm in combination with data collected by field survey. Secondly, seabed mapping was performed for each class separately using spectral and spatial information. Finally, an accuracy assessment of the mapping results was performed using data from field survey. The overall accuracy was 83% with a kappa coefficient of 0.76. The results indicated that the hyperspectral sensing can help not only to classify the seabed material remotely and precisely, but also to construct the continuous geographical information for an effective management and conservation of the coastal area in Korea.