An improved feature extraction algorithm based on KAZE for multi-spectral image

Jianping Yang; Jun Li

doi:10.1117/12.2288731

19 February 2018 An improved feature extraction algorithm based on KAZE for multi-spectral image

Jianping Yang, Jun Li

Proceedings Volume 10608, MIPPR 2017: Automatic Target Recognition and Navigation; 106080Q (2018) https://doi.org/10.1117/12.2288731
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

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.

Citation Download Citation

Jianping Yang and Jun Li "An improved feature extraction algorithm based on KAZE for multi-spectral image", Proc. SPIE 10608, MIPPR 2017: Automatic Target Recognition and Navigation, 106080Q (19 February 2018); https://doi.org/10.1117/12.2288731

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