A Fast Algorithm for Coastline Detection from Remote Sensing Images Based Variational Level Set Methods
In this paper, we present a fast algorithm for coastline detection from remote sensing images based variational level set methods. Our algorithm combines two variational level set methods together, which are a fast algorithm for level set based optimization and the level set evolution without re-initialization. When applying the former algorithm to the Chan-Vese model, it can get a contour that is the outline of the coastline rapidly. Then using the latter model, we evolve the contour as the initial curve. It can sweep out the isolated curve on the land part and preserve the coastline has been detected. The resulting evolution of the contour is the coastline that we detect finally. Our algorithm resolves a conflict between the speed and the accuracy for coastline detection. It can achieve good results in the segmentation in a short time and has the ability to detect the objects from noise images. Several experiments have been conducted on the real images and the numerical results exhibit the proposed algorithm is effective and has good segmentation performance for the coastline detection from remote sensing images.
variational level set methods coastline detection remote sensing image
Dong Liu Ji-tao Wu
LMIB, Key Lab of Education Ministry, Department of Mathematics, Beihang University, Beijing,100083 P.R.China
国际会议
北京国际地理信息系统学术讨论会第七届会议（7th International Workshop Geographical Information System
北京
英文
364-369
2007-09-14（万方平台首次上网日期，不代表论文的发表时间）