A performance evaluation of SIFT and SURF for multispectral image matching

Authors:
Sajid Saleem;Abdul Bais;Robert Sablatnig
Affiliations:
Computer Vision Lab, Institute of Computer Aided Automation, Vienna University of Technology, Vienna, Austria;Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan, Canada;Computer Vision Lab, Institute of Computer Aided Automation, Vienna University of Technology, Vienna, Austria
Venue:
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part I
Year:
2012

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Abstract

This paper evaluates the performance of SIFT and SURF for cross band matching of multispectral images. The evaluation is based on matching a reference spectral image with the images acquired at different spectral bands. The reference image possesses scale and (in-plane) rotational differences in addition to spectral variations. Additive white Gaussian noise is also added to compare performance degradation at different noise levels. We use the precision and repeatability criteria for performance evaluation. Experimental results demonstrate that SIFT performs better than SURF in multispectral environment.