Research papers using Resonon hyperspectral imagers
Hyperspectral Imaging: General
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, No. 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, No. 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 Bioactivity of Surface Pesticide Treatment, 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).
Airborne Hyperspectral Remote Sensing
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) Specialty Group On Camouflage, Concealment & Deception, 1, SENSIAC Military Sensing Information Analysis Center (2007).
Agriculture & Food Technology
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,Fronteirs in Plant Science, 8 article 474 (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).
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 properties of plants and potential for calssification, Pest Management Sci., (2014).
C Nansen, G. Zhao, N. Dakin, C. Zhao, and S.R. Turner,Using hyperspectral imaging to determine germination of native Australian plant seeds, J. Photochem. & Photobio. B, 145 19 (2015).
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 of animal feed quality, Intell. Data Anal., 18, 25 (2014).
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 irrigation regimes, 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, p70 (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 estimate biofuel potential, Applied Spectroscopy, 64, No. 6 (2010).
S. Jay, R. Lawrence, K. Repasky, and C. Keith, Monitoring leafy spurge using a low cost hyperspectral spectrometer, Proc. Am. Soc. of Photogrammetry and Remote Sensing Annual Meeting, Baltimore MD (2009).
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, Int. J. 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 analysis of maize kernels, J. Agric. Food Chem., 14, 2933 (2008).
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, Applied and Environmental Microbiology 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 microbial mat, FEMS Microbial Ecology, 65, 434 (2008).
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. Greenhouse Gas Control, 3, 626, (2009).
L.H. Spangler et al., A shallow subsurface controlled release facility in Bozeman, Montana, USA, for testing near surface CO2 detection techniques and transport models, Environmental Earth Sciences, 60, 227 (2010).
Research and Development Partnerships
We are grateful to partner with the following institutions:
National Aeronautics and Space Administration
National Institutes of Health
National Institute of Standards and Technology
National Oceanic and Atmospheric Administration
National Science Foundation
State of Montana
United States Air Force
United States Department of Agriculture
United States Department of Energy