Version 1
: Received: 17 February 2024 / Approved: 20 February 2024 / Online: 20 February 2024 (09:08:09 CET)
How to cite:
A. Mageed, I. How Satellite Imaging and Deep Learning Are Influenced by Tensor Decompositions: A Review. Preprints2024, 2024021122. https://doi.org/10.20944/preprints202402.1122.v1
A. Mageed, I. How Satellite Imaging and Deep Learning Are Influenced by Tensor Decompositions: A Review. Preprints 2024, 2024021122. https://doi.org/10.20944/preprints202402.1122.v1
A. Mageed, I. How Satellite Imaging and Deep Learning Are Influenced by Tensor Decompositions: A Review. Preprints2024, 2024021122. https://doi.org/10.20944/preprints202402.1122.v1
APA Style
A. Mageed, I. (2024). <strong></strong>How Satellite Imaging and Deep Learning Are Influenced by Tensor Decompositions: A Review. Preprints. https://doi.org/10.20944/preprints202402.1122.v1
Chicago/Turabian Style
A. Mageed, I. 2024 "<strong></strong>How Satellite Imaging and Deep Learning Are Influenced by Tensor Decompositions: A Review" Preprints. https://doi.org/10.20944/preprints202402.1122.v1
Abstract
This study focuses on examining various approaches for Tensor Decompositions (TDs) and their potential applications in satellite imaging (SI) and deep learning (DL). The research highlights how these decompositions can contribute to the advancement of SI and DL, providing valuable insights for future utilization of TDs in research. It explores how these techniques contribute to the advancement of SI and DL methods, providing insights into their potential applications and suggesting future research directions. The study aims to enhance the use of TDs techniques to further advance research efforts in these fields. More importantly, the current investigation offers a thorough analysis of the potential advantages and reasons for employing TDs techniques in different domains, including satellite imaging and deep learning. The aim is to enhance research outcomes by utilizing TDs. The review also identifies unresolved issues and proposes future directions for further investigation in this field. Fundamentally, these proposed open problems will open new grounds to the research community to articulate, innovate, and provide more real-life applications to improve the current state of the art by delving into a wider vision for a higher-level performance of both satellite imaging (SI) and deep learning (DL). Looking at the bigger scenario, this also suggests that TDs could be potentially employed to revolutionize existing machine learning technologies as well as the current space AI industry.
Computer Science and Mathematics, Applied Mathematics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
