Open Access Open Access  Restricted Access Subscription or Fee Access

Hyperspectral Remote Sensing Imagery Processing Focused on Forest Applications

Vladimir V. Kozoderov(1*), Timofei V. Kondranin(2), Egor V. Dmitriev(3)

(1) M.V. Lomonosov Moscow State University, Russian Federation
(2) Moscow Institute of Physics and Technology (State University), Russian Federation
(3) Institute of Numerical Mathematics of Russian Academy of Science, Russian Federation
(*) Corresponding author



Imaging spectrometers with hundreds of spectral channels in visible and infrared regions are designed by various companies to enhance the information content of the relevant hyperspectral imagery processing compared to common-used multispectral systems. We review some sources on this particular subject to show the priorities of the hyperspectral approach before the multispectral one in forest and agriculture applications. There is also a discussion about some results of the information products obtained by an imaging spectrometer produced in Russia for a test area, where the ground-based forest inventory map is available to compare the traditional approaches and the newly defined ones. The related applications concern the pattern recognition of forest classes with different species and age on the test area using the airborne hyperspectral imagery processing.
Copyright © 2017 Praise Worthy Prize - All rights reserved.


Remote Sensing; Multispectral Images; Hyperspectral Images; Pattern Recognition; Forest Vegetation; Agriculture

Full Text:



T. R. Loveland, J. L. Dwyer, Landsat: Building a strong future, Remote Sens. Environ. 122 (2012), 22-29.

B. L. Markham, D. L. Helder, Forty-year calibrated record of earth-reflected radiance from Landsat: A review, Remote Sens. Environ. 122 (2012), 30-40.

M. C. Hansen, T. R. Loveland, A review of large area monitoring of land cover change using Landsat data, Remote Sens. Environ.122 (2012), 66-74.

D. Rogers, T. Tanimoto, A computer program for classifying plants, Science 132 (1960), 1115-1118.

R. E. Osteen, J. T. Tou, A clique detection algorithm based on neighborhoods in graphs, Int. J. Computer Info. Sci. 2, 4 (1973), 257-268.

V. V. Kozoderov, E. V. Dmitriev, V. P. Kamentsev, Cognitive technologies for processing optical images of high spatial and spectral resolution, Atmospheric and Oceanic Optics 27, 6 (2014), 556-563.

M. F. Tompsett, G. F. Amelio, W. J., Jr. Bertram, R. R. Buckley, W. J. McNamara, J. C., Jr. Mikkelsen, D. A. Sealer, Charge-coupled imaging devices: Experimental results, IEEE Transactions on Electron Devices 18, 11 (1971), 992-996.

A. F. H. Goetz, G. Vane, J. E. Solomon, B. N. Rock, Imaging spectrometry for Earth remote sensing, Science 228 (1985), 1147-1153.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, V. P. Kamentsev, A system for processing hyperspectral imagery: application to detecting forest species, Int. J. Remote Sens. 35, 15 (2014), 5926-5945.

E. Binaghi, I. Gallo, M. Pepe, A cognitive pyramid for contextual classification of remote sensing images, IEEE Transactions Geosci. Remote Sens. 41, 12 (2003), 2906-2922.

V. V. Kozoderov, E. V. Dmitriev, V. P. Kamentsev, System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data, Izvestiya, Atmospheric and Oceanic Physics 50, 9 (2014), 943-952.

P. J. Sellers, F. G. Hall, G.Asrar, D. E. Strebel, R. E. Murthy, An Overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), J. Geoph. Res. 97, D17 (1992), 18345-18371.

R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (J. Wiley, 1973).

J. T. Tou, R. C. Gonzalez, Pattern Recognition Principles (Addison-Wesley, 1974).

S. Z. Li, Markov Random Field Modeling in Computer Vision (Springer-Verlag, 1995).

V. V. Kozoderov, Assessment of Effect of the Atmosphere as a Clutter in Recognition of Natural Formations from Space, Airspace Studies of the Earth. Remote Sensing Data Processing Using Computer Means, (Moscow, Nauka, 1978, pp. 24-35).

P. J. Curran, G. M. Foody, K. Ya. Kondratyev, V. V. Kozoderov, P. P. Fedchenko, Remote sensing of soils and vegetation in the USSR (Taylor & Francis, 1990).

K. Ya. Kondratyev, V. V. Kozoderov, O. I. Smokty, Remote Sensing of the Earth from Space: Atmospheric Correction (Springer-Verlag, 1992).

Y. P. Hung, D. B. Cooper, B. Cernuschi-Frias, Asymptotic Bayesian surface estimation using an image sequence, Int. J. Comput. Vis. 6, 2 (1991), 105-132.

A. K. Jain, R. P. W. Duin, J. Mao, Statistical pattern recognition: a review, IEEE Trans. Pattern Anal. Mach. Intell. 22, 1 (2000), 4-37.

L. Herault, R. Horaud, Figure-ground discrimination: A combinatorial optimization approach, IEEE Trans. Pattern Anal. Mach. Intell. 15, 9 (1993), 899-914.

N. S. Friedland, A. Rosenfeld, Compact object recognition using energy-function based optimization, IEEE Trans. Pattern Anal. Mach. Intell. 14, 7 (1992), 770-777.

J. Han, H. Cheng, D. Xin, X. Yan, Frequent pattern mining: current status and future directions, WIREs Data Min. Knowl. Discov. 15, 1 (2007), 55-86.

M. Dalponte, L. Bruzzone, L. Vescovo, D. Gianelle, The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas, Remote Sens. Environ. 113, 11 (2009), 2345-2355.

Y. Dian, Z. Y. Lib, Y. Pang, Forest Tree species Classification Based on Airborne Hyperspectral Imagery, Proc. of SPIE 8921 (2013), 892107-(1-7).

J. Lee et al, Individual Tree Species Classification From Airborne Multisensor Imagery Using Robust PCA, IEEE Journal of Selected topics in applied earth observations and remote sensing 9, 6 (2016), 2554-2567.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, V. P. Kamentsev, Mapping forest and peat fires using hyperspectral airborne remote-sensing data, Izv., Atmos. Ocean. Phys. 48, 9 (2012), 941-948.

N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, first ed. (Cambridge: Cambridge University Press, 2000).

T. P. Higginbottom, E. Symeonakis, Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions, Remote Sens. 6 (2014), 9552-9575.

A. Viña, A. A. Gitelson, A. L. Nguy-Robertson, Y. Peng, Comparison of different vegetation indices for the remote assessment of green leaf area index of crops, Remote Sens. Environ. 115 (2011), 3468-3478.

S. S. Panda, D. P. Ames, Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques, Remote Sens. 2 (2010), 673-696.

F. V. Eroshenko , S. A. Bartalev, I. G. Storchak, D. E. Plotnikov, The possibility of winter wheat yield estimation based on vegetation index of photosynthetic potential derived from remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 13, 4 (2016), 99-112.

V. V. Kozoderov, E. V. Dmitriev, Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment, Int. J. Remote Sens. 32, 3 (2011), 5699-5717.

E. V. Dmitriev, V. V. Kozoderov, Optimization of spectral bands for hyperspectral remote sensing of forest vegetation, Proc. SPIE, Remote Sensing for Agriculture, Ecosystems, and Hydrology, 8887 (2013), 1-10.

M. Marshall, P. Thenkabail, Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation, ISPRS J. of Photogrammetry and Remote Sens. 108 (2015), 205-218.

S. Veraverbeke, E. N. Stavros, S. J. Hook, Assessing fire severity using imaging spectroscopy data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and comparison with multispectral capabilities, Remote Sens. Environ. 154 (2014), 153-163.

S. Hamzeh, A. A. Naserib, S. K. AlaviPanaha, H. Bartholomeusc, M. Herold, Assessing the accuracy of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields, Int. J. of Applied Earth Observation and Geoinformation 52 (2016), 412-421.

R. F. Kokaly, B. W. Rockwell, S. L. Haire, T. V. V. King, Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing, Remote Sens. Environ. 106 (2007), 305-325.

G. V. Laurin, N. Puletti, W. Hawthorne, V. Liesenberg, P. Corona, D. Papale, Q. Chen, R. Valentini, Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data, Remote Sens. Environ. 176 (2016), 163-176.

F. Castaldi, A. Palombo, F. Santini, S. Pascucci, S. Pignatti, R. Casa, Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon, Remote Sens. Environ. 179 (2016), 54-65.

K. S. Lee, W. B. Cohen, R. E. Kennedy, T. K. Maiersperger, S. T. Gower, Hyperspectral versus multispectral data for estimating leaf area index in four different biomes, Remote Sens. Environ. 91 (2004), 508-520.

R. MaNavarro-Cerrillo, J. Trujillo, M.Sánchez de la Orden, R. Hernández-Clemente, Hyperspectral and multispectral satellite sensors for mapping chlorophyll content in a Mediterranean Pinus sylvestris L. plantation, Int. J. of Applied Earth Observ. and Geoinform. 26 (2014), 88-96.

M. Marshall, P. Thenkabail, T. Biggs, K. Post, Hyperspectral narrowband and multispectral broadband indices for remote sensing of crop evapotranspiration and its components (transpiration and soil evaporation), Agricultural and Forest Meteorology 218-219 (2016), 122-134.

M. P. Ferreira, M. Zortea, D. C. Zanotta, Y. E. Shimabukuro, de Souza F. C. R., Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data, Remote Sens. Environ. 179 (2016), 66-78.

I. Mariotto, P. S. Thenkabail, A. Huete, E. T. Slonecker, A. Platonov, Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission, Remote Sens. Environ. 139 (2013), 291-305.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, A. A. Sokolov, Retrieval of forest stand attributes using optical airborne remote sensing data, Optics Express 22, 13 (2014), 15410-15423.

R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, Int. Joint Conf. on Artificial Intell. (IJCAI) (1995) 528-535.

T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning; Data Mining, Inference, and Prediction, second ed. (Springer, 2008).

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, V. P. Kamentsev, A system for processing hyperspectral imagery: application to detecting forest species, Int. J. Remote Sens. 35, 15 (2014), 5926-5945.

V. V. Kozoderov, E. V. Dmitriev, A. A. Sokolov, Improved technique for retrieval of forest parameters from hyperspectral remote sensing data, Optics Express 23, 24 (2015), A1342-A1353.

V. V. Kozoderov, T. V. Kondranin, E. V. Dmitriev, V. P. Kamentsev, Bayesian classifier applications of airborne hyperspectral imagery processing for forested areas, Adv. Space Res. 55, 11 (2015), 2657-2667.

V. V. Kozoderov, E. V. Dmitriev, Testing different classification methods in airborne hyperspectral imagery processing, Optics Express 24, 10 (2016), A956-A965.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. C. Tilton, G. Trianni, Recent advances in techniques for hyperspectral image processing, Remote Sens. Environ. 113 (2009), 110-122.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2019 Praise Worthy Prize