Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis.
| Published in | Communications (Volume 9, Issue 2) |
| DOI | 10.11648/j.com.20210902.11 |
| Page(s) | 6-10 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Deep Learning, Machine Learning, Satellite Image, Quantum Computational Intelligence, Remote Sensing
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APA Style
Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. (2022). Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis. Communications, 9(2), 6-10. https://doi.org/10.11648/j.com.20210902.11
ACS Style
Ibrahim Goni; Asabe Sandra Ahmadu; Yusuf Musa Malgwi. Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis. Communications. 2022, 9(2), 6-10. doi: 10.11648/j.com.20210902.11
AMA Style
Ibrahim Goni, Asabe Sandra Ahmadu, Yusuf Musa Malgwi. Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis. Communications. 2022;9(2):6-10. doi: 10.11648/j.com.20210902.11
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Download@article{10.11648/j.com.20210902.11,
author = {Ibrahim Goni and Asabe Sandra Ahmadu and Yusuf Musa Malgwi},
title = {Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis},
journal = {Communications},
volume = {9},
number = {2},
pages = {6-10},
doi = {10.11648/j.com.20210902.11},
url = {https://doi.org/10.11648/j.com.20210902.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.com.20210902.11},
abstract = {Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis.},
year = {2022}
}
TY - JOUR T1 - Remote Sensing Data Analysis in Machine Learning and Proposed Quantum Computational Intelligence: A Meta-Analysis AU - Ibrahim Goni AU - Asabe Sandra Ahmadu AU - Yusuf Musa Malgwi Y1 - 2022/01/12 PY - 2022 N1 - https://doi.org/10.11648/j.com.20210902.11 DO - 10.11648/j.com.20210902.11 T2 - Communications JF - Communications JO - Communications SP - 6 EP - 10 PB - Science Publishing Group SN - 2328-5923 UR - https://doi.org/10.11648/j.com.20210902.11 AB - Deep learning and machine learning are the top ranking techniques applied in objects classification in remote sensing data. We have conducted a meta-analysis and find out that feature selection is an important achievement in Machine Learning algorithms however, the following challenges were identified; Machine learning need large datasets for training and satellite images contain a lot of noise which may be classify as an object so it is not suitable for object detection in satellite images, Detection accuracy in machine learning depend on the quality of training datasets and finally Biased feature selection may led to the incorrect classification of objects in satellite images. While Most of the deep learning techniques suffer from data preprocessing problems especially when applying in satellite images because satellite images contain a lot of noise. Therefore the requirement of quality and quantity of training datasets is very high. The designed, development, improvement and adjustment of deep learning techniques to suit a specific research is still rely on the experience of the developer which is also a challenging issue. Application of deep learning techniques in remote sense data are still in an infant state because based on our review only few numbers of articles are published from Africa countries. We have suggested that quantum computational intelligence to be applied in remote sensing data analysis. VL - 9 IS - 2 ER -
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