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Article Dans Une Revue Remote Sensing Année : 2023

Polarimetric Imaging via Deep Learning: A Review

Xiaobo Li
  • Fonction : Auteur
Lei Yan
  • Fonction : Auteur
Pengfei Qi
Liping Zhang
  • Fonction : Auteur
Tiegen Liu
  • Fonction : Auteur
Jingsheng Zhai
  • Fonction : Auteur
Haofeng Hu
  • Fonction : Auteur

Résumé

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.

Dates et versions

hal-04492064 , version 1 (06-03-2024)

Identifiants

Citer

Xiaobo Li, Lei Yan, Pengfei Qi, Liping Zhang, François Goudail, et al.. Polarimetric Imaging via Deep Learning: A Review. Remote Sensing, 2023, 15 (6), pp.1540. ⟨10.3390/rs15061540⟩. ⟨hal-04492064⟩
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