Geometric Algebra Applications in Geospatial Artificial Intelligence and Remote Sensing Image Processing

With the increasing demand for multidimensional data processing, Geometric algebra (GA) has attracted more and more attention in the field of geographical information systems. GA unifies and generalizes real numbers and complex, quaternion, and vector algebra, and converts complicated relations and...

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Bibliographic Details
Published in:IEEE access 2020-01, Vol.8, p.1-1
Main Authors: Bhatti, Uzair Aslam, Yu, Zhaoyuan, Yuan, Linwang, Ali Nawaz, Saqib, Bhatti, Mughair, Mehmood, Anum, Ul Ain, Qurat, Zeehsan, Z., Wen, Luo
Format: Article
Language:English
Subjects:
Algebra
Artificial intelligence
Clifford algebra
Computer vision
Coordinates
Data processing
Fourier transforms
Geographic information systems
Geometric algebra
Image classification
Image processing
Multidimensional data
Quaternions
Real numbers
Remote sensing
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Summary:With the increasing demand for multidimensional data processing, Geometric algebra (GA) has attracted more and more attention in the field of geographical information systems. GA unifies and generalizes real numbers and complex, quaternion, and vector algebra, and converts complicated relations and operations into intuitive algebra independent of coordinate systems. It also provides a solution for solving multidimensional information processing with a high correlation among the dimensions and avoids the loss of information. Traditional methods of computer vision and artificial intelligence (AI) provide robust results in multidimensional processing after being combined with GA and give additional feature analysis facility to remote sensing images. In this paper, we provide a detailed review of GA in different fields of AI and computer vision regarding its applications and the current developments in geospatial research. We also discuss the Clifford-Fourier transform (CFT) and quaternions (sub-algebra of GA) because of their necessity in remote sensing image processing. We focus on how GA helps AI and solves classification problems, as well as improving these methods using geometric algebra processing. Finally, we discuss the issues, challenges, and future perspectives of GA with regards to possible research directions.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3018544