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 hyperspectral sensing with a CASI-1500 sensor to map the seabed covers in the coastal area of Korea. After radiometric, geometric and atmospheric correction for the raw hyperspectral data, the classification was performed in three steps. Firstly, fourteen classes of seabed were identified using a unsupervised spectral angle mapping algorithm in combination with data collected by ground survey. Secondly, seabed mapping was performed for each class separately. Finally, an accuracy assessment of the seabed mapping results was performed using data from ground 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.