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.
Polarimetric Imaging via Deep Learning: A Review
Xiaobo Li,Lei Yan,Pengfei Qi,Liping Zhang,F. Goudail,Tiegen Liu,Jingsheng Zhai,Haofeng Hu
Published 2023 in Remote Sensing
ABSTRACT
PUBLICATION RECORD
- Publication year
2023
- Venue
Remote Sensing
- Publication date
2023-03-11
- Fields of study
Computer Science, Engineering, Environmental Science
- Identifiers
- External record
- Source metadata
Semantic Scholar
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
CITED BY
Showing 1-50 of 50 citing papers · Page 1 of 1