Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

Multispectral images (MSIs) are valuable for precision agriculture due to the extra spectral information acquired compared to natural color RGB (ncRGB) images. In this paper, we thus aim to generate high spatial MSIs through a robust, deep-learning-based reconstruction method using ncRGB images. Usi...

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Veröffentlicht in:	Remote Sensing Jg. 14; H. 5; S. 1272
Hauptverfasser:	Zhao, Jiangsan, Kumar, Ajay, Banoth, Balaji Naik, Marathi, Balram, Rajalakshmi, Pachamuthu, Rewald, Boris, Ninomiya, Seishi, Guo, Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 05.03.2022 MDPI
Schlagworte:	[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] [SDE.IE]Environmental Sciences/Environmental Engineering [SDV.SA.STA]Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture Agricultural engineering Agricultural sciences Agriculture Color Color imagery Computer Science Corn data collection Datasets Deep learning Digital cameras Divergence Electrical Engineering Environmental Engineering Environmental Sciences Experiments Growing season hyperspectral imagery Hyperspectral imaging Image acquisition Image enhancement Image Processing Image quality Image reconstruction Life Sciences loss function optimization Machine Learning multispectral image reconstruction multispectral image reconstruction; natural color RGB image; deep learning; loss function optimization; precision agriculture multispectral imagery natural color RGB image normalized difference vegetation index Normalized difference vegetative index phenotype Phenotyping Plant breeding Precision agriculture Q Rice Robustness Science Sciences and technics of agriculture Sensors unmanned aerial vehicles India precision agriculture deep learning natural color RGB image loss function optimization multispectral image reconstruction
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
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