title: A Fast Algorithm to Estimate the Deepest Points of Lakes for Regional Lake Registration.
abstract: When conducting image registration in the U.S. state of Alaska, it is very difficult to locate satisfactory ground control points because ice, snow, and lakes cover much of the ground. However, GCPs can be located by seeking stable points from the extracted lake data. This paper defines a process to estimate the deepest points of lakes as the most stable ground control points for registration. We estimate the deepest point of a lake by computing the center point of the largest inner circle (LIC) of the polygon representing the lake. An LIC-seeking method based on Voronoi diagrams is proposed, and an algorithm based on medial axis simplification (MAS) is introduced. The proposed design also incorporates parallel data computing. A key issue of selecting a policy for partitioning vector data is carefully studied, the selected policy that equalize the algorithm complexity is proved the most optimized policy for vector parallel processing. Using several experimental applications, we conclude that the presented approach accurately estimates the deepest points in Alaskan lakes; furthermore, we gain perfect efficiency using MAS and a policy of algorithm complexity equalization.
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
authors.forename: Zhanfeng
authors.initials: Z
authors.lastname: Shen
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
authors.forename: Xinju
authors.initials: X
authors.lastname: Yu
authors.affiliation: Department of Geography, University of California, Los Angeles, CA 90095-1524, United States of America.
authors.forename: Yongwei
authors.initials: Y
authors.lastname: Sheng
authors.affiliation: Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830020, China.
authors.forename: Junli
authors.initials: J
authors.lastname: Li
authors.affiliation: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
authors.forename: Jiancheng
authors.initials: J
authors.lastname: Luo
mesh_terms: D000465:Algorithms; D060106:Lakes; D014481:United States