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.
Highlights
Precise registration of images and lakes is required for lake change detection and analysis in the North American state of Alaska
This paper presented an efficient center point-seeking algorithm for largest inner circle (LIC), to facilitate regional lake registration
The following algorithm improvements were proposed: first, the medial axis generation algorithm was presented based on the Voronoi generation method, and its simplification method was provided to reduce LIC-seeking computations
Summary
Precise registration of images and lakes is required for lake change detection and analysis in the North American state of Alaska. To perform this task, many ground control points (GCPs), or tie points [1], are required. Because Alaska contains many lakes and is covered with ice and snow much of the year, it is very difficult to locate valid GCPs in multi-phase remotely sensed images or in lake extraction results. The shapes and areas of lakes change significantly over time, it is necessary to locate the most stable points in the lakes as the GCPs. Sheng and Chintan proposed methods that use the centroids of stable lakes as tie points for automated image registration [1, 2].
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