Skip to main content
SpringerLink
Log in

Selection of the Informative Feature System for Crops Classification Using Hyperspectral Data

  • Published:
Optoelectronics, Instrumentation and Data Processing Aims and scope

Abstract

The methods based on processing video data have shown their efficiency in many areas of agriculture and forestry. Nevertheless, they are not enough accurate for classification of barely discernible objects and types of plants, which can be provided only using hyperspectral sensors. However, such devices by now were expensive and difficult in operation and were used mostly on satellites and manned aircraft. In recent years, the technologies for creating smaller and lighter sensors have been proposed; these technologies are based on the choice of restricted number of spectral intervals and their position at the design stage. They may be used for scientific or commercial goals under field conditions and, in addition, installed on unmanned aerial vehicles. Exemplified by a 220-channel hyperspectral image, this paper investigates the capability of significant reduction in the amount of registered data due to choice of position and width of restricted number of most informative spectral channels in solving the classification problem of agricultural plants. It is shown that the applied method for generating feature systems has a considerable advantage against the regular decimation and is close in its efficiency to the methods based on the analysis of principal components, but has a significantly less amount of needed computations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

REFERENCES

  1. Perspective Information Technologies of Earth’s Remote Sensing, Ed. by V. A. Soifer (Novaya Tekhnika, Samara, 2015).

    Google Scholar 

  2. V. G. Bondur, ‘‘Modern approaches to processing large hyperspectral and multispectral aerospace data flows,’’ Izv., Atmos. Ocean. Phys. 50, 840–852 (2014). https://doi.org/10.1134/S0001433814090060

    Article  Google Scholar 

  3. V. V. Shipko, ‘‘Noise filtration in hyperspectral images,’’ Optoelectron., Instrum. Data Process. 56, 19–27 (2020). https://doi.org/10.3103/S8756699020010033

    Article  ADS  Google Scholar 

  4. V. V. Kozoderov, T. V. Kondranin, and E. V. Dmitriev, ‘‘Recognition of natural and man-made objects in airborne hyperspectral images,’’ Izv., Atmos. Ocean. Phys. 50, 878–886 (2014). https://doi.org/10.1134/S0001433814090126

    Article  Google Scholar 

  5. A. N. Vinogradov, V. V. Egorov, A. P. Kalinin, E. M. Melnikova, and A. I. Rodionov, ‘‘The set of hyperspectral sensors in the optical band,’’ Preprint No. Pr-2176, IKI RAN (Space Research Institute, Russian Academy of Sciences, 2015).

    Google Scholar 

  6. N. Tack, A. Lambrechts, S. Soussan, and L. Haspeslagh, ‘‘A compact, high-speed, and low-cost hyperspectral imager,’’ Proc. SPIE 8266, 82660Q (2012). https://doi.org/10.1117/12.908172

    Article  ADS  Google Scholar 

  7. B. Geelen, N. Tack, and A. Lambrechts, ‘‘A snapshot multispectral imager with integrated, tiled filters and optical duplication,’’ Proc. SPIE 8613, 861314 (2013). https://doi.org/10.1117/12.2004072

    Article  Google Scholar 

  8. N. Gat, ‘‘Imaging spectroscopy using tunable filters: a review,’’ Proc. SPIE 4056, 50–64 (2000). https://doi.org/10.1117/12.381686

    Article  ADS  Google Scholar 

  9. J. H. Correia, M. Bartek, and R. F. Wolffenbuttel, ‘‘High-selectivity single-chip spectrometer in silicon for operation in visible part of the spectrum,’’ IEEE Trans. Electron Devices 47, 553–559 (2000). https://doi.org/10.1109/16.824727

    Article  ADS  Google Scholar 

  10. M. Jayapala, A. Lambrechts, N. Tack, B. Geelen, B. Masschelein, and P. Soussan, ‘‘Monolithic integration of flexible spectral filters with CMOS image sensors at wafer level for low cost hyperspectral imaging. http://www.imagesensors.org/Past%20Workshops/2013%20Workshop/2013%20Papers/07-02_053-jayapala.pdf. Cited May 15, 2020.

  11. A. Shvedov, ‘‘Hyperspectral sensors of IMEC company. Solutions for high-quality spectral analysis.’’ https://www.npk-photonica.ru/giperspektralnye-sensory-kompanii-imec-pdf163406.pdf. Cited May 15, 2020.

  12. R. V. Skidanov, A. A. Morozov, A. P. Porfirev, and V. A. Blank, ‘‘An imaging spectrometer based on a discrete interference filter,’’ Comput. Opt. 39, 716–720 (2015). https://doi.org/10.18287/0134-2452-2015-39-5-716-720.

    Article  ADS  Google Scholar 

  13. S. M. Borzov, M. A. Guryanov, and O. I. Potaturkin, ‘‘Study of the classification efficiency of difficult-to-distinguish vegetation types using hyperspectral data,’’ Comput. Opt. 43, 464–473 (2019). https://doi.org/10.18287/2412-6179-2019-43-3-464-473.

    Article  ADS  Google Scholar 

  14. S. M. Borzov, A. O. Potaturkin, O. I. Potaturkin, and A. M. Fedotov, ‘‘Analysis of the efficiency of classification of hyperspectral satellite images of natural and man-made areas,’’ Optoelectron., Instrum. Data Process. 52, 1–10 (2016). https://doi.org/10.3103/S8756699016010015

    Article  Google Scholar 

  15. S. M. Borzov and O. I. Potaturkin, ‘‘Efficiency of the spectral-spatial classification of hyperspectral imaging data,’’ Optoelectron., Instrum. Data Process. 53, 26–34 (2017). https://doi.org/10.3103/S8756699017010058

    Article  ADS  Google Scholar 

  16. I. Fodor, ‘‘A survey of dimension reduction techniques,’’ Technical Report No. UCRL-ID-148494 (Univ. California, Oakland, 2002).

  17. I. A. Pestunov and P. V. Melnikov, ‘‘Principal component analysis and its modifications in hyperspectral image classification,’’ Interexpo Geo-Siberia 4 (2), 45–50 (2015).

    Google Scholar 

  18. M. F. Baumgardner, L. L. Biehl, and D. A. Landgrebe, ‘‘220 band AVIRIS hyperspectral image data set: June 12, 1992 Indian Pine test site 3,’’ (Purdue Univ. Research Repository, 2015). https://doi.org/10.4231/R7RX991C.

  19. T. Merill and D. Green, ‘‘On the effectiveness of receptors in recognition systems,’’ IEEE Trans. Inf. Theory JT, 11–17 (1963). https://doi.org/10.1109/TIT.1963.1057810

  20. P. Ghamisi and J. A. Benediktsson, ‘‘Feature selection based on hybridization of genetic algorithm and particle swarm optimization,’’ IEEE Geosci. Remote Sens. Lett. 12, 309–313 (2015). https://doi.org/10.1109/LGRS.2014.2337320

    Article  ADS  Google Scholar 

  21. D. Beasley, D. R. Bull, and R. R. Martin, ‘‘An overview of genetic algorithms,’’ Univ. Comput. 15 (2), 58–69 (1993).

    Google Scholar 

  22. Yu. L. Barabash, B. V. Varskii, V. T. Zinoviev, et al., Automatic Image Recognition (KVAIU, Kiev, 1964).

    Google Scholar 

  23. J. A. Richards, Remote Sensing Digital Image Analysis (Springer-Verlag, Berlin, 1999).

    Book  Google Scholar 

  24. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods (Cambridge Univ. Press, Cambridge, 2000). https://doi.org/10.1017/CBO9780511801389

  25. C.-C. Chang and C.-J. Lin, ‘‘LIBSVM: A library for support vector machines,’’ ACM Trans. Intell. Syst. Technol. 2, 27 (2011). https://doi.org/10.1145/1961189.1961199

    Article  Google Scholar 

  26. N. G. Zagoruiko, V. N. Elkina, G. S. Lbov, and S. V. Emel’yanov, Package of Applied Programs OTEKS (Finansy i Statistika, Moscow, 1986).

    Google Scholar 

  27. V. N. Elkina, N. G. Zagoruiko, and V. S. Temirkaev, ‘‘Algorithms of directed taxonomic search of informative subsystems of features (DTSA),’’ Vychislit. Syst. Sb. IM SO AN SSSR 59, 49–70 (1974).

    Google Scholar 

  28. Remote Sensing: Qualitative Approach, Ed. by A. S. Alekseev (Nedra, Moscow, 1983).

    Google Scholar 

  29. O. A. Dubrovskaya, T. A. Gurova, and I. A. Pestunov, ‘‘Disease detection on wheat plantings by data of hyperspectral monitoring,’’ in Proc. Int. Conf. Spatial Data Processing Monitoring Prob. Nat. and Human-Made Processes (SDM-2019), Novosibirsk, 2019, pp. 497–500.

  30. A. A. Green, M. Berman, P. Switzer, and M. D. Craig, ‘‘A transformation for ordering multispectral data in terms of image quality with implications for noise removal,’’ IEEE Trans. Geosci. Remote Sens. 26, 65–74 (1988). https://doi.org/10.1109/36.3001

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automation and Electrometry, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    S. M. Borzov & O. I. Potaturkin

Authors
  1. S. M. Borzov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. I. Potaturkin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to S. M. Borzov or O. I. Potaturkin.

Additional information

Translated by E. Oborin

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Borzov, S.M., Potaturkin, O.I. Selection of the Informative Feature System for Crops Classification Using Hyperspectral Data. Optoelectron.Instrument.Proc. 56, 431–439 (2020). https://doi.org/10.3103/S8756699020040032

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S8756699020040032

Keywords:

Search

Navigation