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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| Published in: | Remote Sensing Vol. 14; no. 5; p. 1272 |
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| ISSN: | 2072-4292, 2072-4292 |
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| Abstract | 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. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. |
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| AbstractList | 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. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEloss) and spectral information divergence (SIDloss) were most effective during the building of both models, while models using the MRAEloss function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. 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. Using the data from the agronomic research trial for maize and breeding research trial for rice, we first reproduced ncRGB images from MSIs through a rendering model, Model-True to natural color image (Model-TN), which was built using a benchmark hyperspectral image dataset. Subsequently, an MSI reconstruction model, Model-Natural color to Multispectral image (Model-NM), was trained based on prepared ncRGB (ncRGB-Con) images and MSI pairs, ensuring the model can use widely available ncRGB images as input. The integrated loss function of mean relative absolute error (MRAEₗₒₛₛ) and spectral information divergence (SIDₗₒₛₛ) were most effective during the building of both models, while models using the MRAEₗₒₛₛ function were more robust towards variability between growing seasons and species. The reliability of the reconstructed MSIs was demonstrated by high coefficients of determination compared to ground truth values, using the Normalized Difference Vegetation Index (NDVI) as an example. The advantages of using “reconstructed” NDVI over Triangular Greenness Index (TGI), as calculated directly from RGB images, were illustrated by their higher capabilities in differentiating three levels of irrigation treatments on maize plants. This study emphasizes that the performance of MSI reconstruction models could benefit from an optimized loss function and the intermediate step of ncRGB image preparation. The ability of the developed models to reconstruct high-quality MSIs from low-cost ncRGB images will, in particular, promote the application for plant phenotyping in precision agriculture. |
| Author | Balram Marathi Seishi Ninomiya Wei Guo Balaji Naik Banoth Pachamuthu Rajalakshmi Jiangsan Zhao Ajay Kumar Boris Rewald |
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| Cites_doi | 10.1109/TCI.2020.3014451 10.3390/rs13040729 10.1109/ICCV.2019.00867 10.1109/IS.2018.8710471 10.3390/s19235250 10.1016/j.rse.2020.112012 10.1109/VAST.2017.8585482 10.3390/s19204453 10.3390/app10186467 10.1016/j.ijforecast.2015.03.008 10.1109/ICCV.2017.342 10.1109/LSP.2021.3074082 10.3390/jimaging6090097 10.3390/rs13071260 10.1109/TGRS.2019.2963364 10.1109/CVPRW.2018.00139 10.1109/18.857802 10.2134/agronj2010.0395 10.3390/rs12193258 10.1016/j.rse.2019.01.036 10.1109/JSTARS.2010.2086435 10.3390/rs8121014 10.1109/CVPR.2017.248 10.1080/01431161.2021.1942575 10.1117/1.1766301 10.1109/IGARSS.2018.8518414 10.3354/cr030079 10.3390/rs10071119 10.1109/ICA-ACCA.2018.8609861 10.1007/s12518-019-00292-5 10.3390/rs10010144 10.1016/j.compag.2018.10.017 10.1109/ICCVW.2017.68 10.1007/s11263-021-01539-8 10.1109/ICCV.2015.123 10.1080/01431161.2016.1171929 10.3390/rs11070864 10.1080/01431161.2010.484431 10.1088/1361-6560/ab843e 10.3390/rs13040593 10.1007/978-3-319-46478-7_2 10.3390/s151025663 10.1016/j.rse.2018.04.050 10.1109/WACV.2013.6475015 10.3390/s20216399 10.1007/978-3-030-01249-6_17 10.1145/3449257 10.1016/j.compag.2020.105621 10.1109/TPAMI.2020.3009999 10.1128/aem.56.11.3298-3303.1990 10.1109/CVPRW.2018.00129 10.1002/(SICI)1520-6378(199702)22:1<11::AID-COL4>3.0.CO;2-7 10.3390/rs13071367 10.1002/ppj2.20004 10.1109/HNICEM48295.2019.9072796 10.1109/IGARSS.2008.4780067 10.1016/j.jfoodeng.2016.06.010 10.3390/s20205789 10.3390/rs12233985 10.1007/3-540-47979-1_55 10.5121/ijsc.2015.6101 10.1080/014311697217558 10.1016/0034-4257(74)90003-0 10.1007/978-3-030-03335-4_18 |
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| Contributor | The University of Tokyo (UTokyo) Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche] (BOKU) Universität für Bodenkultur Wien = University of Natural Resources and Life Sciences [Vienne, Autriche] (BOKU) Professor Jayashankar Telangana State Agricultural University (PJTSAU) Indian Institute of Technology [Hyderabad] (IIT Hyderabad) |
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| Keywords | precision agriculture deep learning natural color RGB image loss function optimization multispectral image reconstruction |
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| References | ref_50 Hunt (ref_70) 2011; 103 Liu (ref_43) 2022; 130 Liu (ref_72) 2020; 175 ref_58 ref_13 ref_57 ref_12 Loayza (ref_4) 2020; 12 ref_10 ref_54 ref_53 ref_52 Somasegaran (ref_3) 1990; 56 Fu (ref_23) 2020; 65 ref_51 Sovdat (ref_14) 2019; 225 Morales (ref_37) 2020; 3 ref_19 Nidamanuri (ref_33) 2010; 4 Lowe (ref_5) 2015; 6 ref_18 ref_17 ref_16 DeJonge (ref_2) 2016; 37 ref_59 Willmott (ref_27) 2005; 30 Winkens (ref_60) 2019; 2019 Wei (ref_56) 2020; 6 Seshasai (ref_32) 2011; 32 ref_25 ref_69 ref_22 ref_21 ref_65 ref_64 ref_63 ref_62 Rau (ref_68) 2002; 68 He (ref_74) 2021; 28 ref_29 Zhong (ref_35) 2020; 250 Scepanovic (ref_55) 2021; 5 Hua (ref_41) 2020; 58 Kumar (ref_36) 2021; 23 ref_71 Du (ref_31) 2004; 43 Fu (ref_11) 2022; 44 Huang (ref_73) 2018; 214 ref_34 ref_75 ref_30 Colwell (ref_66) 1974; 3 ref_39 ref_38 Su (ref_15) 2018; 155 Nansen (ref_61) 2016; 190 Gitelson (ref_49) 1997; 18 ref_47 ref_46 ref_45 ref_44 ref_42 Franses (ref_28) 2016; 32 ref_40 ref_1 Fairman (ref_24) 1997; 22 Haichao (ref_67) 2011; 40 ref_48 ref_9 ref_8 Woerd (ref_20) 2015; 15 Chang (ref_26) 2000; 46 Tian (ref_7) 2021; 42 ref_6 |
| References_xml | – volume: 6 start-page: 1233 year: 2020 ident: ref_56 article-title: Deep Recursive Network for Hyperspectral Image Super-Resolution publication-title: IEEE Trans. Comput. Imaging doi: 10.1109/TCI.2020.3014451 – ident: ref_8 doi: 10.3390/rs13040729 – ident: ref_64 doi: 10.1109/ICCV.2019.00867 – ident: ref_16 doi: 10.1109/IS.2018.8710471 – ident: ref_1 doi: 10.3390/s19235250 – volume: 250 start-page: 112012 year: 2020 ident: ref_35 article-title: WHU-Hi: UAV-borne hyperspdectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2020.112012 – ident: ref_51 – ident: ref_17 doi: 10.1109/VAST.2017.8585482 – ident: ref_58 doi: 10.3390/s19204453 – ident: ref_75 doi: 10.3390/app10186467 – volume: 32 start-page: 20 year: 2016 ident: ref_28 article-title: A note on the mean absolute scaled error publication-title: Int. J. Forecast. doi: 10.1016/j.ijforecast.2015.03.008 – ident: ref_54 doi: 10.1109/ICCV.2017.342 – ident: ref_65 – volume: 28 start-page: 957 year: 2021 ident: ref_74 article-title: Mask-ShadowNet: Toward Shadow Removal via Masked Adaptive Instance Normalization publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2021.3074082 – ident: ref_10 doi: 10.3390/jimaging6090097 – ident: ref_57 doi: 10.3390/rs13071260 – volume: 58 start-page: 4558 year: 2020 ident: ref_41 article-title: Relation network for multilabel aerial image classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2019.2963364 – ident: ref_13 doi: 10.1109/CVPRW.2018.00139 – volume: 46 start-page: 1927 year: 2000 ident: ref_26 article-title: An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis publication-title: IEEE Trans. Inf. Theory doi: 10.1109/18.857802 – volume: 103 start-page: 1090 year: 2011 ident: ref_70 article-title: Remote sensing leaf chlorophyll content using a visible band index publication-title: Agron. J. doi: 10.2134/agronj2010.0395 – ident: ref_39 doi: 10.3390/rs12193258 – volume: 225 start-page: 392 year: 2019 ident: ref_14 article-title: Natural color representation of Sentinel-2 data publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2019.01.036 – volume: 4 start-page: 226 year: 2010 ident: ref_33 article-title: Normalized Spectral Similarity Score (NS3) as an Efficient Spectral Library Searching Method for Hyperspectral Image Classification publication-title: IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. doi: 10.1109/JSTARS.2010.2086435 – ident: ref_25 doi: 10.3390/rs8121014 – ident: ref_52 – ident: ref_63 doi: 10.1109/CVPR.2017.248 – volume: 42 start-page: 8840 year: 2021 ident: ref_7 article-title: Assessing rice lodging using UAV visible and multispectral image publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2021.1942575 – volume: 43 start-page: 1777 year: 2004 ident: ref_31 article-title: New hyperspectral discrimination measure for spectral characterization publication-title: Opt. Eng. doi: 10.1117/1.1766301 – ident: ref_19 doi: 10.1109/IGARSS.2018.8518414 – volume: 40 start-page: 1381 year: 2011 ident: ref_67 article-title: Fast seamless mosaic algorithm for multiple remote sensing images publication-title: Infrared Laser Eng. – volume: 30 start-page: 79 year: 2005 ident: ref_27 article-title: Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance publication-title: Clim. Res. doi: 10.3354/cr030079 – ident: ref_18 doi: 10.3390/rs10071119 – ident: ref_71 doi: 10.1109/ICA-ACCA.2018.8609861 – volume: 12 start-page: 247 year: 2020 ident: ref_4 article-title: Development of low-cost remote sensing tools and methods for supporting smallholder agriculture publication-title: Appl. Geomat. doi: 10.1007/s12518-019-00292-5 – ident: ref_40 doi: 10.3390/rs10010144 – volume: 2019 start-page: 31 year: 2019 ident: ref_60 article-title: Automatic shadow detection using hyperspectral data for terrain classification publication-title: Electron. Imaging – volume: 68 start-page: 581 year: 2002 ident: ref_68 article-title: True orthophoto generation of built-up areas using multi-view images publication-title: Photogramm. Eng. Remote Sens. – volume: 155 start-page: 157 year: 2018 ident: ref_15 article-title: Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.10.017 – ident: ref_22 doi: 10.1109/ICCVW.2017.68 – volume: 130 start-page: 179 year: 2022 ident: ref_43 article-title: Semantic edge detection with diverse deep supervision publication-title: Int. J. Comput. Vis. doi: 10.1007/s11263-021-01539-8 – ident: ref_45 doi: 10.1109/ICCV.2015.123 – volume: 37 start-page: 2294 year: 2016 ident: ref_2 article-title: Assessing corn water stress using spectral reflectance publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2016.1171929 – ident: ref_30 doi: 10.3390/rs11070864 – ident: ref_34 – volume: 32 start-page: 4041 year: 2011 ident: ref_32 article-title: A new hybrid spectral similarity measure for discrimination among Vigna species publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2010.484431 – volume: 65 start-page: 20TR01 year: 2020 ident: ref_23 article-title: Deep learning in medical image registration: A review publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/ab843e – ident: ref_38 doi: 10.3390/rs13040593 – ident: ref_12 doi: 10.1007/978-3-319-46478-7_2 – volume: 15 start-page: 25663 year: 2015 ident: ref_20 article-title: True color classification of natural waters with medium-spectral resolution satellites: SeaWiFS, MODIS, MERIS and OLCI publication-title: Sensors doi: 10.3390/s151025663 – volume: 214 start-page: 73 year: 2018 ident: ref_73 article-title: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.04.050 – ident: ref_69 doi: 10.1109/WACV.2013.6475015 – ident: ref_44 – ident: ref_21 doi: 10.3390/s20216399 – ident: ref_42 doi: 10.1007/978-3-030-01249-6_17 – volume: 5 start-page: 1 year: 2021 ident: ref_55 article-title: Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data publication-title: Proc. ACM Hum. Comput. Interact. doi: 10.1145/3449257 – volume: 175 start-page: 105621 year: 2020 ident: ref_72 article-title: Hyperspectral imaging and 3D technologies for plant phenotyping: From satellite to close-range sensing publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2020.105621 – ident: ref_6 – volume: 44 start-page: 256 year: 2022 ident: ref_11 article-title: Joint camera spectral response selection and hyperspectral image recovery publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2020.3009999 – volume: 23 start-page: 100549 year: 2021 ident: ref_36 article-title: Efficient Maize Tassel-Detection Method using UAV based Remote Sensing publication-title: Remote Sens. Appl. Soc. Environ. – volume: 56 start-page: 3298 year: 1990 ident: ref_3 article-title: Ben Single-strain versus multistrain inoculation: Effect of soil mineral N availability on rhizobial strain effectiveness and competition for nodulation on chick-pea, soybean, and dry bean publication-title: Appl. Environ. Microbiol. doi: 10.1128/aem.56.11.3298-3303.1990 – ident: ref_29 – ident: ref_46 doi: 10.1109/CVPRW.2018.00129 – volume: 22 start-page: 11 year: 1997 ident: ref_24 article-title: How the CIE 1931 color-matching functions were derived from Wright-Guild data publication-title: Color Res. Appl. doi: 10.1002/(SICI)1520-6378(199702)22:1<11::AID-COL4>3.0.CO;2-7 – ident: ref_9 doi: 10.3390/rs13071367 – volume: 3 start-page: e20004 year: 2020 ident: ref_37 article-title: ImageBreed: Open-access plant breeding web–database for image-based phenotyping publication-title: Plant Phenome J. doi: 10.1002/ppj2.20004 – ident: ref_50 doi: 10.1109/HNICEM48295.2019.9072796 – ident: ref_48 doi: 10.1109/IGARSS.2008.4780067 – volume: 190 start-page: 34 year: 2016 ident: ref_61 article-title: Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes publication-title: J. Food Eng. doi: 10.1016/j.jfoodeng.2016.06.010 – ident: ref_53 doi: 10.3390/s20205789 – ident: ref_62 doi: 10.3390/rs12233985 – ident: ref_59 doi: 10.1007/3-540-47979-1_55 – volume: 6 start-page: 1 year: 2015 ident: ref_5 article-title: Multispectral image analysis using random forest publication-title: Int. J. Soft Comput. Sci. doi: 10.5121/ijsc.2015.6101 – volume: 18 start-page: 2691 year: 1997 ident: ref_49 article-title: Remote estimation of chlorophyll content in higher plant leaves publication-title: Int. J. Remote Sens. doi: 10.1080/014311697217558 – volume: 3 start-page: 175 year: 1974 ident: ref_66 article-title: Vegetation canopy reflectance publication-title: Remote Sens. Environ. doi: 10.1016/0034-4257(74)90003-0 – ident: ref_47 doi: 10.1007/978-3-030-03335-4_18 |
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