A Remote Sensing Image Retrieval Method Based on Quaternion Transformation

XU Yanyan; ZHAO Xiao; LI Zijun

doi:10.13203/j.whugis20170290

Volume 44 Issue 11

Nov. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(11): 1633-1640. > DOI: 10.13203/j.whugis20170290

XU Yanyan, ZHAO Xiao, LI Zijun. A Remote Sensing Image Retrieval Method Based on Quaternion Transformation[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1633-1640. DOI: 10.13203/j.whugis20170290

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A Remote Sensing Image Retrieval Method Based on Quaternion Transformation

1.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Fanruan Software Co. Ltd, Wuxi 450002, China

Funds:

The National Key Research and Development Program of China 2017YFB0504202

the National Natural Science Foundation of China 41571426

Wuhan Applied Basic Research Programs 2017010201010114

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Author Bio:
XU Yanyan, professor, PhD, specializes in cloud computing security and big data privacy protection, multimedia network communications. E-mail:xuyy@whu.edu.cn
Received Date: March 08, 2018
Published Date: November 04, 2019

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Abstract

Abstract

Remote sensing image retrieval requires that image features have the properties of rotation and scale invariance. Currently different color channels are often processed separately during the process of feature extraction, and unique geometric features of remote sensing image are utilized inadequately, which decrease image retrieval precision. A remote sensing image retrieval method based on quaternion transformation is proposed. Aiming at solving the problem of texture features extracted based on quaternion orthogonal Fourier-Mellin moments only have rotation invariance, we construct texture feature with rotation and scale invariance based on quaternion orthogonal Fourier-Mellin moments. We also use quaternion orthogonal Fourier-Mellin moment to detect image edges, get edge color image and extract edge color histogram, and integrate different features for image retrieval. The experimental results show that the proposed method has good robustness to image rotation and scale, and has better retrieval performance than other methods.
- quaternion,
- remote sensing image retrieval,
- orthogonal Fourier-Mellin moments,
- edge color histogram

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