RT Journal Article
T1 Design of CGAN Models for Multispectral Reconstruction in Remote Sensing
A1 Rodríguez Suárez, Brais
A1 Quesada Barriuso, Pablo
A1 Argüello Pedreira, Francisco
K1 Spectral reconstruction
K1 Multispectral image
K1 Deep learning
K1 CGAN
AB Multispectral imaging methods typically require cameras with dedicated sensors that make them expensive. In some cases, these sensors are not available or existing images are RGB, so the advantages of multispectral processing cannot be exploited. To solve this drawback, several techniques have been proposed to reconstruct the spectral reflectance of a scene from a single RGB image captured by a camera. Deep learning methods can already solve this problem with good spectral accuracy. Recently, a new type of deep learning network, the Conditional Generative Adversarial Network (CGAN), has been proposed. It is a deep learning architecture that simultaneously trains two networks (generator and discriminator) with the additional feature that both networks are conditioned on some sort of auxiliary information. This paper focuses the use of CGANs to achieve the reconstruction of multispectral images from RGB images. Different regression network models (convolutional neuronal networks, U-Net, and ResNet) have been adapted and integrated as generators in the CGAN, and compared in performance for multispectral reconstruction. Experiments with the BigEarthNet database show that CGAN with ResNet as a generator provides better results than other deep learning networks with a root mean square error of 316 measured over a range from 0 to 16,384.
PB MDPI
SN 2072-4292
YR 2022
FD 2022-02-09
LK https://hdl.handle.net/10347/41310
UL https://hdl.handle.net/10347/41310
LA eng
NO Rodríguez-Suárez, B., Quesada-Barriuso, P., & Argüello, F. (2022). Design of CGAN Models for Multispectral Reconstruction in Remote Sensing. "Remote Sensing", 14(4), 816. https://doi.org/10.3390/rs14040816
NO This work was supported in part by the Ministerio de Ciencia e Innovación, Government of Spain (grant number PID2019-104834 GB-I00), and Consellería de Educación, Universidade e Formación Profesional (grant number ED431C 2018/19, and accreditation 2019–2022 ED431G-2019/04). All are co-funded by the European Regional Development Fund (ERDF).
DS Minerva
RD 23 abr 2026