Polarimetric imaging via deep learning: a review
Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the incre...
| Main Authors: | , , , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/169472 |
| _version_ | 1826120753912741888 |
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| author | Li, Xiaobo Yan, Lei Qi, Pengfei Zhang, Liping François,Goudail Liu, Tiegen Zhai, Jingsheng Hu, Haofeng. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Li, Xiaobo Yan, Lei Qi, Pengfei Zhang, Liping François,Goudail Liu, Tiegen Zhai, Jingsheng Hu, Haofeng. |
| author_sort | Li, Xiaobo |
| collection | NTU |
| description | Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field. |
| first_indexed | 2024-10-01T05:21:57Z |
| format | Journal Article |
| id | ntu-10356/169472 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:21:57Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1694722023-07-21T15:40:35Z Polarimetric imaging via deep learning: a review Li, Xiaobo Yan, Lei Qi, Pengfei Zhang, Liping François,Goudail Liu, Tiegen Zhai, Jingsheng Hu, Haofeng. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Polarization Polarimetric Imaging Polarization can provide information largely uncorrelated with the spectrum and intensity. Therefore, polarimetric imaging (PI) techniques have significant advantages in many fields, e.g., ocean observation, remote sensing (RS), biomedical diagnosis, and autonomous vehicles. Recently, with the increasing amount of data and the rapid development of physical models, deep learning (DL) and its related technique have become an irreplaceable solution for solving various tasks and breaking the limitations of traditional methods. PI and DL have been combined successfully to provide brand-new solutions to many practical applications. This review briefly introduces PI and DL’s most relevant concepts and models. It then shows how DL has been applied for PI tasks, including image restoration, object detection, image fusion, scene classification, and resolution improvement. The review covers the state-of-the-art works combining PI with DL algorithms and recommends some potential future research directions. We hope that the present work will be helpful for researchers in the fields of both optical imaging and RS, and that it will stimulate more ideas in this exciting research field. Published version This work was supported by the National Natural Science Foundation of China (62205243, 62075161) 2023-07-19T08:05:10Z 2023-07-19T08:05:10Z 2023 Journal Article Li, X., Yan, L., Qi, P., Zhang, L., François, G., Liu, T., Zhai, J. & Hu, H. (2023). Polarimetric imaging via deep learning: a review. Remote Sensing, 15(6), 1540-. https://dx.doi.org/10.3390/rs15061540 2072-4292 https://hdl.handle.net/10356/169472 10.3390/rs15061540 2-s2.0-85151487904 6 15 1540 en Remote Sensing © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Polarization Polarimetric Imaging Li, Xiaobo Yan, Lei Qi, Pengfei Zhang, Liping François,Goudail Liu, Tiegen Zhai, Jingsheng Hu, Haofeng. Polarimetric imaging via deep learning: a review |
| title | Polarimetric imaging via deep learning: a review |
| title_full | Polarimetric imaging via deep learning: a review |
| title_fullStr | Polarimetric imaging via deep learning: a review |
| title_full_unstemmed | Polarimetric imaging via deep learning: a review |
| title_short | Polarimetric imaging via deep learning: a review |
| title_sort | polarimetric imaging via deep learning a review |
| topic | Engineering::Electrical and electronic engineering Polarization Polarimetric Imaging |
| url | https://hdl.handle.net/10356/169472 |
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