MU-Net: Deep Learning-Based Thermal IR Image Estimation From RGB Image

Yumi Iwashita, Kazuto Nakashima, Sir Rafol, Adrian Stoica, Ryo Kurazume; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

Abstract


Terrain imagery collected by satellite remote sensing or by rover on-board sensors is the primary source for terrain classification used in determining terrain traversibility and mission plans for planetary rovers. Mapping models between RGB and IR for terrain classes are learned from real RGB and IR data examples in the same or similar terrain. This paper adds a new class of deep learning architectures called MU-Net (Multiple U-Net) and shows its efficiency in deriving better RGB-to-IR mapping models, improving over past work the estimation of thermal IR images from incoming RGB images and learned RGB-IR mappings.

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[bibtex]
@InProceedings{Iwashita_2019_CVPR_Workshops,
author = {Iwashita, Yumi and Nakashima, Kazuto and Rafol, Sir and Stoica, Adrian and Kurazume, Ryo},
title = {MU-Net: Deep Learning-Based Thermal IR Image Estimation From RGB Image},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2019}
}