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Publication

Auto-adaptive Harris corner detection algorithm based on entropy-improved block processing...

by Yihang Sun, Emmett Ientilucci, Sophie Voisin
Publication Type
Conference Paper
Journal Name
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Book Title
Proceeding of SPIE
Publication Date
Page Number
1064414
Volume
10644
Conference Name
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Conference Location
Orlando, Florida, United States of America
Conference Sponsor
SPIE
Conference Date
-

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.