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Proceedings Paper

Improvement of the Harris corner detector using an entropy-block-based strategy
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Paper Abstract

Extracting well distributed control points (CPs) is a very challenging task for remote sensing image registration, particularly for large high-resolution images over heterogeneous landscape. Based on image analysis such as edge detection, corner detection, and information theory, a new CP detection approach is proposed to select high- quality, evenly distributed CPs. The Entropy-Block-Based variant of the Harris Corner Detector (EBB-HCD) is achieved by dividing the image into blocks and by allocating the number of CP's based upon the entropy of each block. While the block-based strategy improves the CP balance problem, a factor calculated from entropy avoids overdetection. We conducted a comparison study utilizing the well-known Harris Corner Detector (HCD) and an implementation of the Block-Based Harris Corner Detector (BB-HCD). Experimental results indicate that using EBB-HCD to find the CPs improves the overall alignment accuracy during registration compared with HCD or BB-HCD.

Paper Details

Date Published: 8 May 2018
PDF: 9 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 1064414 (8 May 2018); doi: 10.1117/12.2305733
Show Author Affiliations
Yihang Sun, Rochester Institute of Technology (United States)
Emmett Ientilucci, Rochester Institute of Technology (United States)
Sophie Voisin, Oak Ridge National Lab. (United States)


Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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