Hierarchical Sea-Land Segmentation for Panchromatic Remote Sensing Imagery

Abstract

Automatic sea-land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high-resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea-land segmentation approach for practical use with a hierarchical coarse-to-fine procedure. We compared our method with other state-of-the-art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.