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Proceedings Paper

An improved feature extraction algorithm based on KAZE for multi-spectral image
Author(s): Jianping Yang; Jun Li
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Paper Abstract

Multi-spectral image contains abundant spectral information, which is widely used in all fields like resource exploration, meteorological observation and modern military. Image preprocessing, such as image feature extraction and matching, is indispensable while dealing with multi-spectral remote sensing image. Although the feature matching algorithm based on linear scale such as SIFT and SURF performs strong on robustness, the local accuracy cannot be guaranteed. Therefore, this paper proposes an improved KAZE algorithm, which is based on nonlinear scale, to raise the number of feature and to enhance the matching rate by using the adjusted-cosine vector. The experiment result shows that the number of feature and the matching rate of the improved KAZE are remarkably than the original KAZE algorithm.

Paper Details

Date Published: 19 February 2018
PDF: 6 pages
Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080Q (19 February 2018); doi: 10.1117/12.2288731
Show Author Affiliations
Jianping Yang, Guilin Univ. of Electronic Technology (China)
Jun Li, Guilin Univ. of Electronic Technology (China)


Published in SPIE Proceedings Vol. 10608:
MIPPR 2017: Automatic Target Recognition and Navigation
Jianguo Liu; Jayaram K. Udupa; Hanyu Hong, Editor(s)

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