Research Publications in Hyperspectral Imaging

Research Publications


Below are research publications describing investigations using Resonon hyperspectral systems.

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Agriculture & Food Technology
Research and Development Partnerships
Hyperspectral Imaging: General
Airborne Hyperspectral Remote Sensing
Environmental Monitoring

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

Agriculture & Food Technology

Michael B. Farrar, Helen M. Wallace, Iman Tahmasbian, Catherine M. Yule, Peter K. Dunn, Shahla Hosseini Bai, Rapid assessment of soil carbon and nutrients following application of organic amendments, Catena 223, 106928 (2023).
Abdulridha J, Ampatzidis Y, Qureshi J, and Roberts P, Identification and Classification of Downy Mildew Severity Stages in Watermelon Utilizing Aerial and Ground Remote Sensing and Machine Learning , Frontiers in Plant Science , 20 May (2022).
Zongpeng Li , Zhen Chen , Qian Cheng , Fuyi Duan , Ruixiu Sui, Xiuqiao Huang, and Honggang Xu , UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat , Agronomy 12(1), 202 (2022).
Shubhangi Srivastava and Hari Niwas Mishra, Detection of insect damaged rice grains using visible and near infrared hyperspectral imaging technique, Chemometrics and Intelligent Laboratory Systems 221, 104489 (2022).
Lucas Costa, Jordan McBreen, Yiannis Ampatzidis, Jia Guo, Mostafa Reisi Gahrooei, Md Ali Babar , Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes, Precision Agriculture 23, 622-642 (2022).
Shengnan Wang, Yong Tan, Chunyu Liu, Shaozhong Song, Zheng Li, Variety Classification and Identification of Soybean Seeds Based on Hyperspectral Imaging Method, Journal of Sensor Technology and Application 10(2), 177-186 (2022).
Wenyang Jia, Saskia van Ruth, Nigel Scollan, and Anastasios Koidis, Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends, Current Research in Food Science 5, 1017-1027 (2022).
Hongyu Liu, Fuheng Qu, Yong Yang, Wanting Li, and Zhonglin Hao, Soybean Variety Identification Based on Improved ResNet18 Hyperspectral Image, Journal of Physics: Conference Series 2284, 012017 (2022).
Xiang Yun, Chen Qijun, Su Zhongjing, Zhang Lu, Chen Zuohui, Zhou Guozhi, Yao Zhuping, Xuan Qi, and Cheng Yuan, Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation, Frontiers in Plant Science 13, 860656 (2022).
Hu Y, Wang Z, Li X, Li L, Wang X, Wei Y., Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms, Sensors 22(16), 6064 (2022).
Li, Zongpeng and Chen, Zhen and Cheng, Qian and Duan, Fuyi and Sui, Ruixiu and Huang, Xiuqiao and Xu, Honggang, UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat, Agronomy 12(1), 202 (2022).
Iost Filho, Fernando Henrique and de Bastos Pazini, Juliano and de Medeiros, André Dantas and Rosalen, David Luciano and Yamamoto, Pedro Takao, Assessment of Injury by Four Major Pests in Soybean Plants Using Hyperspectral Proximal Imaging, Agronomy 12(7), 1516 (2022).
Mónica Pineda and Matilde Barón , Health Status of Oilseed Rape Plants Grown under Potential Future Climatic Conditions Assessed by Invasive and Non-Invasive Techniques, Agronomy 12(8), 1845 (2022).
Myongkyoon Yang, Physiological Disorder Diagnosis of Plant Leaves Based on Full-Spectrum Hyperspectral Images with Convolutional Neural Network, Horticulturae 8(9), 854 (2022).
Eshkabilov, Sulaymon, John Stenger, Elizabeth N. Knutson, Erdem Küçüktopcu, Halis Simsek, and Chiwon W. Lee, Hyperspectral Image Data and Waveband Indexing Methods to Estimate Nutrient Concentration on Lettuce (Lactuca sativa L.) Cultivars, Sensors 22(21), 8158 (2022).
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, 12081234 (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, Applied Sciences 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: 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 Parasitism, Front. Physiol. 9, 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, 01182 (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, 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, 01102 (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).

Research and Development Partnerships

Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen, Hamide Kavak, Basita Das, and Tonio Buonassisi, Vision-driven Autocharacterization of Perovskite Semiconductors, arXiv:2304.14408v1 , 16 Mar (2023).


Ramy Abdlaty, Mohamed A. Abbass, and Ahmed M. Awadallah, Radiofrequency ablation for liver: Comparison between expert eye and hyperspectral imaging assessment, Photodiagnosis and Photodynamic Therapy 37, 102699 (2022).
Karin Kjernsmoa, Anna M. Lim, Rox Middleton, Joanna R. Hall, Leah M. Costello, Heather Whitney, Nicholas E. Scott-Samuel, and Innes C. Cuthill , Beetle iridescence induces an avoidance response in naïve avian predators, Animal Behaviour, Volume 188, June 2022, Pages 45-50 188, 45-50 (2022).
Mohamed Hisham Fouad Aref, Mohamed A. Abbass, Abou-Bakr M. Youssef, Abdallah Abdelkader Hussein, Sara Abd El-Ghaffar, and Ramy Abdlaty, Optical Characterization of Biological Tissues in Visible and Near-Infrared Spectra, 13th International Conference on Electrical Engineering (ICEENG) , 9781827 (2022).
Susannah M. Leahy, Dean R. Jerry, Brett B.C. Wedding, Julie B. Robins, Carole L. Wright, Aleksey Sadekov, Stephen Boyle, David B. Jones, Samuel M. Williams, Malcolm T. McCulloch, Steve Grauf, Luke Pavich, Mark McLennan, Michelle J. Sellin, Julie A. Goldsbury, Richard J. Saunders, Barramundi origins: Determining the contribution of stocking to the barramundi catch on Queensland’s east coast, Fisheries Research and Development Corporation 2018-047 Final Report , (2022).
Ramy Abdlaty, Mohamed A. Abbass, Ahmed M. Awadallah, Radiofrequency ablation for liver: Comparison between expert eye and hyperspectral imaging assessment, Photodiagnosis and Photodynamic Therapy 37, 102699 (2022).
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, 537 (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).

Hyperspectral Imaging: General

Marc Vermeulen, Annette S. Ortiz Miranda, Diego Tamburini, Sol E. Rivera Delgado, and Marc Walton, A multi-analytical study of the palette of impressionist and post-impressionist Puerto Rican artists , Heritage Science 10, 44 (2022).
Shuhei Watanabe, Multi-angle measurement device for analysis of coating appearances, Color Research and Appllications 47(1), 27-39 (2022).
Marc Vermeulen, Alicia McGeachy, Bingjie Xu, Henry Chopp, Aggelos Katsaggelos, Rebecca Meyers, Matthias Alfeld and Marc Walton, XRFast and new software package for processing of MA-XRF datasets using machine learning, Journal of Analytical Atomic Spectrometry 37, 2130-2143 (2022).
Marc Vermeulen, Diego Tamburini, Alicia C. McGeachy, Rebecca D. Meyers, Marc S. Walton, Multiscale characterization of shellfish purple and other organic colorants in 20th-century traditional enredos from Oaxaca, Mexico, Dyes and Pigments 206, 110663 (2022).
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 , 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 , June (2020).
L. Logan and J. Shaw, Measuring the polarization response of a VNIR hyperspectral imager, Proc. SPIE 11412, 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, 2515641 (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).
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).

Airborne Hyperspectral Remote Sensing

Sureka Thiruchittampalam, Simit Raval, Nancy Glenn, and Furqan Le-Hussain, Indirect remote sensing techniques for long term monitoring of CO2 leakage in geological carbon sequestration: A review, Journal of Natural Gas Science and Engineering 100, 104488 (2022).
Zhou, Quan, Linfeng Yu, Xudong Zhang, Yujie Liu, Zhongyi Zhan, Lili Ren, and Youqing Luo, Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678), Remote Sensing 14 no. 10 , 2428 (2022).
Mohamed Karim El Oufir, Karem Chokmani , Anas El Alem, and Monique Bernier, Using Ensemble-Based Systems with Near-Infrared Hyperspectral Data to Estimate Seasonal Snowpack Density, Remote Sensing 14 no. 5, 1089 (2022).
R Ganesh Babu and C Chellaswamy, Different stages of disease detection in squash plant based on machine learning, Journal of Biosciences 47, 9 (2022).
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds, Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements, The Cryosphere 16, 43-59 (2022).
Quanzhou Yu, Robert A. Mickler, Tianquan Liang, Yujie Liu, Jie Jiang, Kaishan Song, and Shaoqiang Wang, Hyperspectral differences between sunlit and shaded leaves in a Manchurian ash canopy in Northeast China, Remote Sensing Letters 13:8, 800-811 (2022).
Bowen Niu, Quanlong Feng, Boan Chen, Cong Ou, Yiming Liu, Jianyu Yanga, HSI-TransUNet: A transformer based semantic segmentation model for crop mapping from UAV hyperspectral imagery, Computers and Electronics in Agriculture 201, 107297 (2022).
Costa, L., McBreen, J., Ampatzidis, Y. et al., Using UAV-based hyperspectral imaging and functional regression to assist in predicting grain yield and related traits in wheat under heat-related stress environments for the purpose of stable yielding genotypes, Precision Agriculture 23, 622-642 (2022).
Zhou Quan, Linfeng Yu, Xudong Zhang, Yujie Liu, Zhongyi Zhan, Lili Ren, and Youqing Luo, Fusion of UAV Hyperspectral Imaging and LiDAR for the Early Detection of EAB Stress in Ash and a New EAB Detection Index—NDVI(776,678), Remote Sensing 14(10), 2428 (2022).
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).

Environmental Monitoring

Jonathan Teague, David A. Megson-Smith, Michael J. Allen, John C.C. Day, and Thomas B. Scott , A Review of Current and New Optical Techniques for Coral Monitoring, Oceans 3(1), 30-45 (2022).
Francisco Rodríguez Lorenzo, Miguel Placer Lorenzo, Luz Herrero Castilla, Juan Antonio Álvarez Rodríguez, Sandra Iglesias, Santiago Gómez, Juan Manuel Fernández Montenegro, Estel Rueda, Rubén Diez-Montero, Joan Garcia and Eva Gonzalez-Flo, Monitoring PHB production in Synechocystis sp. with hyperspectral images, Sater Science and Technology 86 No1, 211 (2022).
Christopher Donahue and Kevin Hammonds , Laboratory Observations of Preferential Flow Paths in Snow Using Upward-Looking Polarimetric Radar and Hyperspectral Imaging, Remote Sensing 14(10), 2297 (2022).
Joseph Michael Odhiambo, Dr. Mgala Mvurya, Dr. Anthony Luvanda, Dr. Fullgence Mwakondo, Deep Learning Algorithm for Identifying Microplastics in Open Sewer Systems: A Systematic Review, The International Journal of Engineering and Science 11-5, 11-18 (2022).
Liang Xinlian, Kukko Antero, Balenovic Ivan, Saarinen Ninni, Junttila Samuli, Kankare Ville, Holopainen Markus, Mokroš Martin, Surový Peter, Kaartinen Luka, Honkavaara Eija, Näsi Jingbin, Hollaus Markus, Tian Jiaojiao, Yu Xiaowei, Pan Jie, Cai Shangshu, Virtanen Juho-Pekk, Wang Yunsheng, Hyyppä Juha, Close-Range Remote Sensing of Forests: The state of the art, challenges, and opportunities for systems and data acquisitions, IEEE Geoscience and Remote Sensing Magazine 10-3, 32-71 (2022).
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).
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