Research Publications in Hyperspectral Imaging

Research Publications


Below are research publications describing investigations using Resonon hyperspectral systems.

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Hyperspectral Imaging: General
Agriculture & Food Technology
Environmental Monitoring
Airborne Hyperspectral Remote Sensing

Institutional Partners

We are grateful for support from the following institutions:
National Institutes of Health
National Institutes of Health
National Institute of Standards and Technology
National Institute of Standards and Technology
National Oceanic and Atmospheric Administration
National Oceanic and Atmospheric Administration
National Science Foundation
National Science Foundation
State of Montana
State of Montana
United States Air Force
United States Air Force
Department of Agriculture
Department of Agriculture
Department of Energy
Department of Energy

Hyperspectral Imaging: General

D.J. Lary, D. Schaefer, J. Waczak, A. Aker, A. Barbosa, L.O.H. Wijeratne, S. Talebi, B. Fernando, J. Sadler, T. Lary and M..D. Lary, Autonomous Learning of New Environments with a Robotic Team Employing Hyper-Spectral Remote Sensing, Comprehensive In-Situ Sensing and Machine Learning, Sensors 21, 2240 (2021).
N.V. Scott and J. McCarthy, Potassium Phosphorus Nitrate Detection and Spectral Segmentation Using Polarized Hyperspectral Imagery in High Reflective Glare Environments, Presentation at 9th Annual Forensic Science Symposium. The Global Forensic and Justice Center, June (2020).
N.V. Scott and S. Jensen, Blood Stain Detection on Camouflage Clothing Using Machine Learning Analysis of Noisy Hyperspectral Imagery, Presentation at 9th Annual Forensic Science Symposium, The Global Forensic and Justice Center, June (2020).
L. Logan and J. Shaw, Measuring the polarization response of a VNIR hyperspectral imager, Proc. SPIE 11412, doi: 10.1117/12.2558257 (2020).
C. Zhu, YU. Kanaya, R. Nakajima, M. Tsuchiya, H. Nomaki, T. Kitahashi, and K. Fujikura, Characterization of microplastics on filter substrates based on hyperspectral imaging: Laboratory assessments, Environmental Pollution 263, 114296 (2020).
J. Striova, A. Dal Fovo, and R. Fontana, Reflectance imaging spectroscopy in heritage science, La Rivista del Nuovo Cimento 43, 515 (2020).
M.Á. Martínez-Domingo, A.I. Castillo, E.V. García, and E.M. Valero, Evaluation of Cleaning Processes Using Colorimetric and Spectral Data for the Removal of Layers of Limewash from Medieval Plasterwork, Sensors 20, 7147 (2020).
M.A. Martinez, E.M. Valero, J.L. Nieves, R. Blanc, E. Manzano, and J.L. Vilchez, Multifocus HDR VIS/NIR hyperspectral imaging and its application to works of art, Opt. Express 27, 11323 (2019).
R.C. Swanson, W.S. Kirk, G.C. Dodge, M. Kehoe, and C. Smith, Anamorphic imaging spectrometers, Proc. SPIE 1098005, (2019).
C. Nansen, Penetration and scattering: Two optical phenomena to consider when applying proximal remote sensing technologies to objects, PLoS ONE 13, e0204579 (2018).
W. Castro, J. Oblitas, J. Maicelo, and H. Avila-George, Evaluation of Expert Systems Techniques for Classifying Different Stages of Coffee Rust Infection in Hyperspectral Images, Int. J. Comput. Intell. Sys. 11, 86-100 (2018).
E. Pouyet, N. Rohani, A.K. Katsaggelos, O. Cossairt and M. Walton, Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach, Pure Appl. Chem. 90, 493 (2018).
P. Knipe, K. Eremin, M. Walton, A. Babini, and G. Rayner, Materials and techniques of Islamic manuscripts, Herit. Sci. 6, 55 (2018).
C.I. Zhao, B. Qi, and C. Nansen, Use of local fuzzy variance to extract the scattered regions of spatial stray light influence in hyperspectral images, Optik 124, 6696 (2013).
C. Nansen, Robustness of analyses of imaging data, Optics Express 19, 16 (2011).
B. Qi, C. Zhao, E. Youn, and C. Nansen, Use of weighting algorithms to improve traditional support vector machine based classifications of reflectance data, Optics Express 19, 27 (2011).
C. Nansen, N. Abidi, A.J. Sidumo, and A.H. Gharalari, Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bi, Remote Sensing 2, 908 (2010).
P.W. Nugent, J.A. Shaw, M.R. Kehoe, C.W. Smith, T.S. Moon, and R.C. Swanson, Measuring the modulation transfer function of an imaging spectrometer with rooflines of opportunity, Optical Engineering 49, 103201 (2010).
R.C. Swanson, T.S. Moon, C.W. Smith, M.R. Kehoe, S.W. Brown, and K.R. Lykke, Anamorphic Imaging Spectrometer, Proc. SPIE 6940, 694010 (2008).
P.W. Nugent, J.A. Shaw, M.R. Kehoe, C.W. Smith, T.S. Moon, and R.C. Swanson, Measuring the MTF of imaging spectrometers at infinite focus with roofline images, Proc. SPIE 6661, 1 (2007).

Agriculture & Food Technology

I. Tahmasbian, N.K. Morgan, S.H. Bai , M.W. Dunlop, and A.F. Moss, Comparison of Hyperspectral Imaging and Near-Infrared Spectroscopy to Determine Nitrogen and Carbon Concentrations in Wheat, Remote Sens. 13, 1128 (2021).
W. Yang, T. Nigon, Z. Hao, G.D. Paiao, F.G. Fernandez, D. Mulla, and C. Yang, Estimation of corn yield based on hyperspectral imagery and convolutional neural network, Comp. Elec. in Ag. 184, 106092 (2021).
Z. Yang, J. Tian, K. Feng, X. Gong, and J. Liu, Application of a hyperspectral imaging system to quantify leaf-scale chlorophyll, nitrogen and chlorophyll fluorescence parameters in grapevine, Plant Phys. and Biochem. 166, 723 (2021).
Y. Zhang, J. Hui, Q. Qin, Y. Sun, T. Zhang, H. Sun, and M. Li, Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data, Remote Sens. Env. 267, 112724 (2021).
S. Yang, L. Hu, H. Wu, H. Ren, H. Qiao, P. Li, and W. Fan, Integration of Crop Growth Model and Random Forest for Winter Wheat Yield Estimation From UAV Hyperspectral Imagery, IEEE J. Selected Topics Appl. Earth Obs. Remote Sens. 14, 6253 (2021).
T.J. Nigon, et al., Prediction of Early Season Nitrogen Uptake in Maize Using High-Resolution Aerial Hyperspectral Imagery, Remote Sens. 12, (2020).
K. Zhu, et al., Remotely sensed canopy resistance model for analyzing the stomatal behavior of environmentally-stressed winter wheat, ISPRS J. Phot. Remote Sens. 168, 197 (2020).
Y. Wei, X. Li, X. Pan, and L. Li, Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms, Sensors 20, 6980 (2020).
M.A. Faqeerzada, M. Perez, S. Lohumi, H. Lee, G. Kim, C. Wakholi, R. Joshi, and B.K. Cho, Online Application of a Hyperspectral Imaging System for the Sorting of Adulterated Almonds, Appl. Sci. 10, 6569 (2020).
A. Moghimi, C. Yang, J.A. Anderson, Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat, eprint arXiv:1906.09666 , arXiv: arXiv:1906.09666 (2019).
S. Yang, L. Hu, H. Wu, W. Fan, and H. Ren, Estimation Model of Winter Wheat Yield Based on Uav Hyperspectral Data, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , 7212 (2019).
J. Zhang, Y. Huang, K.N. Reddy, and B. Wang, Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning, Pest Manag. Sci. 75, 3260 (2019).
X. Ji'An, C. HongXin, Y. YuWang, Z. WeiXin, W. Qian, X. Lei, G. DaoKuo, Z. WenYu, K. YaQi, and H. Bo, Detection of waterlogging stress based on hyperspectral images of oilseed rape leaves (Brassica napus L.), Computers and Electronics in Agriculture 159, 59 (2019).
L. Ribeiro, A. Klock, J. Filho, M. Tramontin, A. Trapp, A. Mithofer, and C. Nansen, Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory, Plant Methods 14:54, (2018).
P.W. Nugent, J.A. Shaw, P. Jha, B. Scherrer, A. Donelick, and V. Kumar, Discrimination of herbicide-resistant kochia with hyperspectral imaging, J. of Appl. Remote Sens. 12(1), 016037 (2018).
Y. Huang, M.A. Lee, V .K. Nandula, and K.N. Reddy, Hyperspectral Imaging for Differentiating Glyphosate-Resistant and Glyphosate-Susceptible Italian Ryegrass, Am. J. Plant Sci. 9, 1467 (2018).
M. Kanning, I. Kühling, D. Trautz, and T. Jarmer, High-Resolution UAV-Based Hyperspectral Imagery for LAI and Chlorophyll Estimations from Wheat for Yield Prediction, Remote Sensing 10, 2000 (2018).
C. Nansen and M. R. Strand, Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasi, Front. Physiol. 9:1716. doi: 10.3389/fphys.2018.01716, (2018).
A. Moghimi, C. Yang, M.E. Miller, S.F. Kianian, and P.M. Marchetto, A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging, Front. Plant Sci. 24, (2018).
A. Moghimi, C. Yang, and P.M. Marchetto, Ensemble Feature Selection for Plant Phenotyping: A Journey From Hyperspectral to Multispectral Imaging, IEEE Access 6, doi: 10.1109/ACCESS.2018.2872801 (2018).
S. Gutiérrez, J. Fernández-Novales, M.P. Diago and J. Tardaguila, On-The-Go Hyperspectral Imaging Under Field Conditions and Machine Learning for the Classification of Grapevine Varieties, Front. Plant Sci. 9, (2018).
S. Gutierrez, J. Tardaguila, J. Fernandez-Novales and M.P. Diago, On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration, Aust. J. Grape Wine Res. 18, 173-182 (2018).
N. Vasquez a, C. Magan, J. Oblitas, T. Chuquizuta, H. Avila-George, and W. Castro, Comparison between artificial neural network and partial least squares regression models for hardness modeling during the ripening process of Swiss-type cheese using spectral profiles, J. Food 219, 8-15 (2018).
W. Castro, J.M. Prieto, R. Guerra, T. Chuquizuta, W.T. Medina, B. Acevedo-Juarez, and H. Avila-George, Feasibility of using spectral profiles for modeling water activity in five varieties of white quinoa grains, J. Food Eng. 238, 95-102 (2018).
M. Morin, R. Lawrence, K. Repasky, T. Sterling, C. McCann, and S. Powell, Agreement analysis and spatial sensitivity of multispectral and hyperspectral sensors in detecting vegetation stress at , J. of Appl. Remote Sens. 11(4) , 046025 (2017).
C. McCann, K.S. Repasky, R. Lawrence, and S. Powell, Multi-temporal mesoscale hyperspectral data of mixed agricultural and grassland regions for anomaly detection, ISPRS Journal of Photogrammetry and Remote Sensing 131, 121 (2017).
Li X, Xu H, Feng L, Fu X, Zhang Y, Nansen C, Using proximal remote sensing in non-invasive phenotyping of invertebrates, PLoS One 12(5), e0176392 (2017).
M. Matzrafi, I. Herrmann, C. Nansen, T. Kliper, Y. Zait, T. Ignat, D. Siso, B. Rubin, A. Karnieli, and H. Eizenberg, Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri, Frontiers in Plant Science 8, 474 (2017).
V. Aredo, L. Velasquez, and R. Siche, Prediction of beef marbling using Hyperspectral Imagin (HSI) and Partial Least Squares Regression (PLSR), Scientia Agropecuaria 8, 169-174 (2017).
C. Nansen, K. Singh, A. Mian, B.J. Allison, C.W. Simmons, Using hyperspectral imaging to characterize consistency of coffee brands and their respective roasting classes, J. Food Eng. 190, 34-39 (2016).
C Nansen, G. Zhao, N. Dakin, C. Zhao, and S.R. Turner, Using hyperspectral imaging to determine germination of native Australian plant seeds, Journal of Photochemistry & Photobiology B 145, 19 (2015).
M.A. Lee, Y. Huang, V.K. Nandula, and K.N. Reddy, Differentiating glyphosate-resistant and glyphosate-sensitive Italian ryegrass using hyperspectral imagery, Proc. SPIE 9108, (2014).
K.N. Reddy, Y. Huang, M.A., Lee, V.K. Nandula, R.S. Fletcher, S.J. Thomson and F. Zhao, Glyphosate-resistant and glyphosate-susceptible Palmer amaranth (Amaranthus palmeri S. Wats.): hyperspectral reflectance, Pest Management Science , (2014).
C. Nansen, X. Zhang, N. Aryamanesh, and G. Yan, Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation, J. Food Eng. 123, 17 (2014).
P. Wilcox, T.M. Horton, E.Youn, M.K. Jeong, D. Tate, T. Herman, and C. Nansen, Evolutionary refinement approaches for band selection of hyperspectral images with applications to automatic monitoring , Intelligent Data Analysis 18, 25 (2014).
C.I. Zhao, B. Qi, and C. Nansen, Use of local fuzzy variance to extract the scattered regions of spatial stray light influence in hyperspectral images, Optik 124, 6696 (2013).
C. Nansen, A.J. Sidumo, X. Martini, K. Stefanova, and J.D. Roberts, Reflectance-based assessment of spider mite "bio-response" to maize leaves and plant potassium content in different irri, Computers and Electronics in Agriculture 97, 21 (2013).
C. Nansen, Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves, Remote Sensing 4, 180 (2012).
C. Nansen, S. Prager, B. Qi, X. Martini, M. Lewis, and K. Vaugn, Using Hyperspectral Imaging in ZC Research, Proc. 11th Annual SCRI Zebra Chip Reporting Session , 70 (2011).
C. Nansen, T. Herrman, and R. Swanson, Machine Vision Detection of Bonemeal in Animal Feed Samples, Applied Spectroscopy 64, 637 (2010).
C. Nansen, A.J. Sidumo, and S. Capareda, Vairogram analysis of hyperspectral data to characterize the impact of biotic and abiotic stress of maize plans and to e, Applied Spectroscopy 64, 6 (2010).
S. Jay, R. Lawrence, K. Repasky, and C. Keith, Invasive species mapping using low cost hyperspectral imagery, ASPRS Annual Conference, Baltimore MD , (2009).
C. Nansen, T. Macedo, R. Swanson, and D.K. Weaver, Use of spatial structure analysis of hyperspectral data cubes for detection of insect-induced stress in wheat plants, International Journal of Remote Sensing 30, 2447 (2009).
C. Nansen, M. Kolomeits, and X. Gao, Considerations regarding the use of hyperspectral imaging data in classifications of food products, exemplified by analy, J. of Ag. and Food Chem. 14, 2933 (2008).

Environmental Monitoring

R. Yu, Y. Luo, Q. Zhou, X. Zhang, D. Wu, and L. Ren, A machine learning algorithm to detect pine wilt disease using UAV-based hyperspectral imagery and LiDAR data at the tree level , Int. J. Appl. Ear. Obs. Geo. 101, 102363 (2021).
H. Qin, W. Zhou, Y. Yao, and W. Wang, Estimating Aboveground Carbon Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data, Rem. Sens. 13, 4969 (2021).
W. Pi, Y. Bi, J. Du, H. Yang, X Zhang, and Y. Kang, Classification of Grassland Desertification in China Based on Vis-NIR UAV Hyperspectral Remote Sensing, Spectroscopy 35, 39 (2020).
C. Donahue, S.M. Skiles, and K. Hammonds, In situ effective snow grain size mapping using a compact hyperspectral imager, Journal of Glaciology doi: 10.1017/jog.2020.68, 1-9 (2020).
J. Lekki, S. Ruberg, C. Binding, R. Anderson, and A. VanderWoude, Airborne hyperspectral and satellite imaging of harmful algal blooms in the Great Lakes Region: Successes in sensing algal blooms, J. Great Lakes Research 45, 405 (2019).
N. Scott and I. Moore, Nonnegative matrix factorization based feature selection analysis for hyperspectral imagery of sediment-laden riverine flow, Proc. SPIE 10631, doi: 10.1117/12.2301273 (2018).
A. Chennu, P. Farber, N. Volkenborn, M.A.A. Al-Najjar, F. Janssen, D. deBeer, and L. Polerecky, Hyperspectral imaging of the microscale distribution and dynamics of microphytobenthos in intertidal sediments, Limnol. Oceanogr.: Methods 11, 511 (2013).
L.H. Spangler et al, A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techni, Env. Earth Sci. 60, 227 (2010).
CJ Keith, KS Repasky, RL Lawrence, SC Jay, JL Carlsten, Monitoring effects of a controlled subsurface carbon dioxide release on vegetation using a hyperspectral imager, Int. J. of Greenhouse Gas Control 3, 626 (2009).


P. Fu, K. Meacham-Hensold, M.H. Siebers, and C.J. Bernacci, The inverse relationship between solar-induced fluorescence yield and photosynthetic capacity: benefits for field phenotyping, J. Experimental Botany Dec, (2020).
K. Nagasubramanian1, S. Jones, A. K. Singh, S. S., A. Singh, and B. Ganapathysubramanian, Plant disease identification using explainable 3D deep learning on hyperspectral images, Plant Methods 15, (2019).
M. Guillemort, R. Midahuen, D. Archeny, C. Fulchiron, R. Montvernay, G. Perrin, and D. Leroux, Hyperspectral imaging for presumptive identification of bacterial colonies on solid chromogenic culture media, Proc. SPIE 9887, 98873L (2016).
D. Leroux, R. Midahuen, G. Perrin, J. Pescatore, and P. Imbaud, Hyperspectral imaging applied to microbial categorization in an automated microbiology workflow, Proc. SPIE-OSA 9537, 953726 (2015).
K. Barott, J. Smith, E. Dinsdale, M. Hatay, S. Sandin, and F. Rohwer, Hyperspectral and Physiological Analyses of Coral Algal Interactions, PloS ONE 4, e8043 (2009).
L. Polerecky, A. Bissett, M. Al-Najjar, P. Faerber, H. Osmers, P.A. Suci, P. Stoodley, and D. de Beer, Modular spectral imaging system for discrimination of pigments in cells and microbial communities, Appl. and Env. Microbio. 75, 758 (2009).
M. Kühl and L. Polerecky, Functional and structural imaging of phototrophic microbial communities and symbioses, Aquatic Microbial Ecology 53, 99 (2008).
A. Bachar, L. Polerecky, J.P. Fischer, K. Vamvakopoulos, D. de Beer, and H.M. Jonkers, Two-dimensional mapping of photopigment distribution and activity of Chloroflexus-like bacteria in a hypersaline microbi, FEMS Microbial Ecology 65, 434 (2008).

Airborne Hyperspectral Remote Sensing

J. Abdulridha, O. Batuman, and Y. Ampatzidis, UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning, Remote Sensing 11, 1373 (2019).
C. Zhihu, T. Qiangqian, Z. Zeying, and Y. Yanbin, Research on hyperspectral remote sensing image acquisition and processing technology based on uav, Journal of Guizhou Normal University (Natural Sciences) 37, 52-57 (2019).
R. Hruska, J. Mitchell, M. Anderson and N.F. Glenn, Radiometric and Geometric Analysis of Hyperspectral Imagery Acquired from an Unmanned Aerial Vehicle, Remote Sensing 4, 2736 (2012).
M.C.L. Patterson and A. Brescia, Operation of small sensor payloads on tactical sized unmanned air vehicles, Aeronautical Journal 114, 427 (2010).
R.C. Swanson, M.R. Kehoe, C.W. Smith, T.S. Moon, R. Bousquet, S.W. Brown, K.R. Lykke, P. Maciejewski, and K. Barnard, Compact Anamorphic Imaging Spectrometer, 2007 Meeting of the Military Sensing Symposia (MSS) Speciality Group On Camouflage, Concealment & Deception 1, (2007).

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