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Computer Software for Selected Plant Species Segmentation on Airborne Images

  • Sebastian IwaszenkoEmail author
  • Marcin Kelm
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 928)

Abstract

The usage of manned and unmanned flying devices for aerial images acquisition becomes more and more popular nowadays. Though unmanned aerial vehicles (aka drones) are capable of an easy and efficient gathering of spatial information, there is a growing need for efficient tools supporting its analysis and processing. The software solution presented in this article is aimed at identification and characterization of the selected plant species on industrial waste dumps based on aerial images analysis. The software uses back projection method for segmentation of areas covered by Solidago canadensis (Goldenrod), which is known as an invasive one. The software assists the user in the segmentation of areas covered by the identified plants and characterizing their parameters. The application implements selected methods helping in image preprocessing, segmentation, weed identification and calculation of segmented areas shape parameters. The Solidago canadensis segmentation tests were performed with satisfactory results.

Notes

Acknowledgments

The research presented in the paper have been supported by the statutory activity of the Central Mining Institute: Application of image recognition and machine learning methods for Run-Of-Mine and waste mineral matter characterization – No. GIG: 11010117-144.

References

  1. 1.
    Dorigo, W., Lucieer, A., Podobnikar, T., Čarni, A.: Mapping invasive fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos. Int. J. Appl. Earth Obs. Geoinf. 19, 185–195 (2012)CrossRefGoogle Scholar
  2. 2.
    Draeyer, B., Strecha, C.: White paper: how accurate are UAV surveying methods? Pix4D (2014). https://pix4d.com/wp-content/uploads/2016/11/Pix4D-White-paper_How-accurate-are-UAV-surveying-methods.pdf
  3. 3.
    Eisenbeiß, H.: UAV photogrammetry. Ph.D. thesis, ETH Zurich (2009)Google Scholar
  4. 4.
    European Parliment: Regulation (EU) No 1143/2014 of the European Parliament and of the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species (2014)Google Scholar
  5. 5.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements Of Reusable Object-oriented Software. Pearson Education, London (1994)zbMATHGoogle Scholar
  6. 6.
    Gao, J.: Digital Analysis of Remotely Sensed Imagery. McGraw-Hill Professional, New York City (2008)Google Scholar
  7. 7.
    Hill, D.J., Tarasoff, C., Whitworth, G.E., Baron, J., Bradshaw, J.L., Church, J.S.: Utility of unmanned aerial vehicles for mapping invasive plant species: a case study on yellow flag iris (Iris pseudacorus l.). Int. J. Remote Sens. 38(8–10), 2083–2105 (2017)CrossRefGoogle Scholar
  8. 8.
    Ketcham, D.J., Lowe, R.W., Weber, J.W.: Image enhancement techniques for cockpit displays (1974)Google Scholar
  9. 9.
    Lawrence, R.L., Wood, S.D., Sheley, R.L.: Mapping invasive plants using hyperspectral imagery and breiman cutler classifications (RandomForest). Remote Sens. Environ. 100(3), 356–362 (2006)CrossRefGoogle Scholar
  10. 10.
    Lee, J.h., Lee, W.h., Jeong, D.S.: Object tracking method using back-projection of multiple color histogram models. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS 2003, vol. 2, pp. II-II. IEEE (2003)Google Scholar
  11. 11.
    Lu, B., He, Y.: Species classification using unmanned aerial vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland. ISPRS J. Photogrammetry Remote Sens. 128, 73–85 (2017)CrossRefGoogle Scholar
  12. 12.
    Lucas, R., Bunting, P., Paterson, M., Chisholm, L.: Classification of australian forest communities using aerial photography, casi and HyMap data. Remote Sens. Environ. 112(5), 2088–2103 (2008)CrossRefGoogle Scholar
  13. 13.
    Lucas, R., Rowlands, A., Brown, A., Keyworth, S., Bunting, P.: Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping. ISPRS J. Photogrammetry Remote Sens. 62(3), 165–185 (2007)CrossRefGoogle Scholar
  14. 14.
    Mathieu, R., Aryal, J.: Object-oriented classification and ikonos multispectral imagery for mapping vegetation communities in urban areas. In. In Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre, pp. 181–188 (2005)Google Scholar
  15. 15.
    Michez, A., Piégay, H., Jonathan, L., Claessens, H., Lejeune, P.: Mapping of riparian invasive species with supervised classification of unmanned aerial system (UAS) imagery. Int. J. Appl. Earth Obs. Geoinf. 44, 88–94 (2016)CrossRefGoogle Scholar
  16. 16.
    Müllerová, J., Pergl, J., Pyšek, P.: Remote sensing as a tool for monitoring plant invasions: testing the effects of data resolution and image classification approach on the detection of a model plant species heracleum mantegazzianum (giant hogweed). Int. J. Appl. Earth Obs. Geoinf. 25, 55–65 (2013)CrossRefGoogle Scholar
  17. 17.
    Nurzynska, K., Kubo, M., Muramoto, K.I.: Shape parameters for automatic classification of snow particles into snowflake and graupel. Meteorol. Appl. 20(3), 257–265 (2013)CrossRefGoogle Scholar
  18. 18.
    Nurzynska, K., Smolka, B.: PCA application in classification of smiling and neutral facial displays. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 398–407. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-18422-7_35CrossRefGoogle Scholar
  19. 19.
    Olszewski, P., Grabowski, J., Kelm, M.: Evaluation of resources and dynamics of plant communities of invasive species in forestland and post industrial areas. In: 16th International Multidiciplinary Scientific Geoconference SGEM 2016, vol. 3, pp. 423–430. Surveying Geology & Mining Ecology Management (SGEM) (2016)Google Scholar
  20. 20.
    Olszewski, P., Grabowski, J., Kelm, M.: Using remotely piloted aircraft systems for the evaluation of resources of large-area communities of invasive species. Wiadomości Melioracyjne i Łąkarskie (452), 16–23 (2017)Google Scholar
  21. 21.
    Ørka, H.O., Hauglin, M.: Use of remote sensing for mapping of non-native conifer species. INA Fagrapport 33, 76 (2016)Google Scholar
  22. 22.
    Pimentel, D., Lach, L., Zuniga, R., Morrison, D.: Environmental and economic costs of nonindigenous species in the united states. BioScience 50(1), 53–65 (2000)CrossRefGoogle Scholar
  23. 23.
    Pouteau, R., Meyer, J.Y., Stoll, B.: A SVM-based model for predicting distribution of the invasive tree miconia calvescens in tropical rainforests. Ecol. Model. 222(15), 2631–2641 (2011)CrossRefGoogle Scholar
  24. 24.
    Reinhardt, F., Herle, M., Bastiansen, F., Streit, B., et al.: Economic impact of the spread of alien species in germany. Federal Environmental Agency (Umweltbundesamt), Berlin, Germany (2003)Google Scholar
  25. 25.
    Robinson, T., Wardell-Johnson, G., Pracilio, G., Brown, C., Corner, R., Van Klinken, R.: Testing the discrimination and detection limits of WorldView-2 imagery on a challenging invasive plant target. Int. J. Appl. Earth Obs. Geoinf. 44, 23–30 (2016)CrossRefGoogle Scholar
  26. 26.
    Species, D.: Handbook of Alien Species in Europe. Invading Nature - Springer Series in Invasion Ecology. Springer, Netherlands (2008).  https://doi.org/10.1007/978-1-4020-8280-1. https://books.google.pl/books?id=_g-syyoXw2gC
  27. 27.
    Suzuki, S.: Topological structural analysis of digitized binary images by border following. Comput. Vis. Graph. Image Process. 30(1), 32–46 (1985)CrossRefGoogle Scholar
  28. 28.
    Swain, M.J., Ballard, D.H.: Indexing via color histograms. In: Sood, A.K., Wechsler, H. (eds.) Active Perception and Robot Vision, pp. 261–273. Springer, Heidelberg (1992).  https://doi.org/10.1007/978-3-642-77225-2_13CrossRefGoogle Scholar
  29. 29.
    Tokarska-Guzik, B., Dajdok, A., Zając, M., Zając, A., Urbisz, A., Danielewicz, W., Hołdyński, C.: Rośliny obcego pochodzenia w Polsce (Plants of foreign origin in Poland with particular reference to invasive species, in polish). Warszawa: Generalna Dyrekcja Ochrony Środowiska (2012)Google Scholar
  30. 30.
    Yan, D., de Beurs, K.M.: Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the random forest classification algorithm. Int. J. Appl. Earth Obs. Geoinf. 47, 125–138 (2016)CrossRefGoogle Scholar
  31. 31.
    Yang, C., Everitt, J.H.: Mapping three invasive weeds using airborne hyperspectral imagery. Ecol. Inform. 5(5), 429–439 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Central Mining InstituteKatowicePoland
  2. 2.KELM Solutions Marcin KelmKatowicePoland

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