Document Type

Article

Publication Date

2021

DOI

10.3390/rs13163257

Publication Title

Remote Sensing

Volume

13

Issue

16

Pages

3257 (1-23)

Abstract

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos.

Comments

© 2021 by the Authors.

This is an open access article distributed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

Uddin, M. S., Hoque, R., Islam, K. A., Kwan, C., Gribben, D., & Li, J. (2021). Converting optical videos to infrared videos using attention GAN and its impact on target detection and classification performance. Remote Sensing, 13(16), 1-23, Article 3257. https://doi.org/10.3390/rs13163257

ORCID

0000-0002-2466-1212 (Uddin)

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