Automatic extraction of parallel edges based on eigenvalue analysis and collateral expansion

Abstract

In the research field of remote sensing (RS), automatic extraction of parallel edges (AEPE) usually serves as the kernel when implementing automatic recognition of strip-like objects, such as rivers, highways, canyons and canals. The traditional algorithms proposed for edge detection commonly assume the mathematical singularity of boundaries as their theoretical basis and are also required to be all-prevalent, but the associated results cannot deal with the desired collection of parallel-only edges in most specific applications of RS data. To solve this problem, this article advances a novel algorithm for AEPE, which proceeds with the output edges from the existing edge-extraction methods as targets. The new algorithm comprises the procedure of eigenvalue analysis with improvements for extracting line segments, the procedure of collateral expansion for removing the non-parallel line segments recursively, and finally the procedure of segment connection. Finally, experiments based on both RS panchromatic images and light detection and ranging (LiDAR)-rastered images validate the new AEPE algorithm, with an efficacy greater than 70% and an accuracy better than 90%.