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Cluster Computing

, Volume 22, Supplement 6, pp 13569–13581 | Cite as

Modified Genetic Algorithm (MGA) based feature selection with Mean Weighted Least Squares Twin Support Vector Machine (MW-LSTSVM) approach for vegetation classification

  • V. Shenbaga PriyaEmail author
  • D. Ramyachitra
Article
  • 95 Downloads

Abstract

Vegetation classification using remotely sensed images is an advancing approach predominantly in area developmental schemes. It is very common that the same vegetation type on ground may have different spectral features in remotely sensed images. Also, different vegetation types may possess similar spectra, which makes very hard to obtain accurate classification results. In the recent work, there are number of classifiers that are proposed by different researchers to solve this problem. Though many solutions are available, high dimensionality of samples become a major issue. The prime objective of this work is to increase the classification efficiency of agricultural area. This research presents a novel object identification and feature selection algorithm. At the initial stage of the work, Modified Fuzzy Possibilistic C-Means clustering is applied for the proficient segmentation of objects. In addition texture and the spectral features of the segmented image are extracted for efficient vegetation classification and these features are selected based on the Modified Genetic Algorithm based wrapper feature selection algorithm. Finally, vegetation classification is performed by using Mean Weight-Least Squares Twin Support Vector Machine (MW-LSTSVM). Thus the vegetation classification is achieved accurately. The experimentation results prove that the MW-LSTSVM provides higher values in regard to accuracy, recall, precision and F-measure justifying its efficiency. MW-LSTSVM efficiently improves the classification of remotely sensed images in an agricultural area when compared to existing classifiers.

Keywords

Vegetation classification Wrapper feature selection Clustering Segmentation Modified Fuzzy Possibilistic C-Means (MFPCM) Modified Genetic Algorithm (MGA) Mean Weight-Least Squares Twin Support Vector Machine (MW-LSTSVM) classifier 

References

  1. 1.
    Sandmann, H., Lertzman, K.P.: Combining high-resolution aerial photography with gradient-directed transects to guide field sampling and forest mapping in mountainous terrain. For. Sci. 49(3), 429–443 (2003)Google Scholar
  2. 2.
    Harvey, K.R., Hill, G.J.E.: Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photography, Landsat TM and SPOT satellite imagery. Int. J. Remote Sens. 22(15), 2911–2925 (2001)CrossRefGoogle Scholar
  3. 3.
    Czaplewski, R.L., Patterson, P.L.: Classification accuracy for stratification with remotely sensed data. For. Sci. 49(3), 402–408 (2003)Google Scholar
  4. 4.
    Ehlers, M., Gahler, M., Janowsky, R.: Automated analysis of ultra high-resolution remote sensing data for biotope type mapping: new possibilities and challenges. ISPRS J. Photogramm. Remote Sens. 57(5–6), 315–326 (2003)CrossRefGoogle Scholar
  5. 5.
    Benediktsson, J.A., Pesaresi, M., Arnason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)CrossRefGoogle Scholar
  6. 6.
    Herold, M., Gardner, M.E., Roberts, D.A.: Spectral resolution requirements for mapping urban areas. IEEE Trans. Geosci. Remote Sens. 41(9), 1907–1919 (2003)CrossRefGoogle Scholar
  7. 7.
    Carleer, A., Wolff, E.: Exploitation of very high resolution satellite data for tree species identification. Photogramm. Eng. Remote Sens. 70(1), 135–140 (2004)CrossRefGoogle Scholar
  8. 8.
    Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS J. Photogramm. Remote Sens. 58(3), 225–238 (2004)CrossRefGoogle Scholar
  9. 9.
    Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D.: Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sens. 72(7), 799–811 (2006)CrossRefGoogle Scholar
  10. 10.
    Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q.: Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115(5), 1145–1161 (2011)CrossRefGoogle Scholar
  11. 11.
    Hay, G.J., Marceau, D.J., Dube, P., Bouchard, A.: A multiscale framework for landscape analysis: object-specific analysis and upscaling. Landsc. Ecol. 16(6), 471–490 (2001)CrossRefGoogle Scholar
  12. 12.
    Mohammad-Djafari, A., Mohammadpour, A., Feron, O.: Segmentation of hyperspectral images. In: Proceedings of the 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP, San José, CA, USA (2005)Google Scholar
  13. 13.
    Peña, J.M., Gutiérrez, P.A., Hervás-Martínez, C., Six, J., Plant, R.E., López-Granados, F.: Object-based image classification of summer crops with machine learning methods. Remote Sens. 6(6), 5019–5041 (2014)CrossRefGoogle Scholar
  14. 14.
    Rutzinger, M., Höfle, B., Hollaus, M., Pfeifer, N.: Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification. Sensors 8(8), 4505–4528 (2008)CrossRefGoogle Scholar
  15. 15.
    Cleve, C., Kelly, M., Kearns, F.R., Moritz, M.: Classification of the wildland–urban interface: a comparison of pixel-and object-based classifications using high-resolution aerial photography. Comput. Environ. Urban Syst. 32(4), 317–326 (2008)CrossRefGoogle Scholar
  16. 16.
    Niemeyer, I., Canty, M.J.: Pixel-based and object-oriented change detection analysis using high-resolution imagery. In: Proceedings of 25th Symposium on Safeguards and Nuclear Material Management, Stockholm, 13–15 May 2003Google Scholar
  17. 17.
    Castillejo-Gonzalez, I.L., Lopez-Granados, F., Garcia-Ferrer, A., Pena-Barragan, J.M., Jurado-Exposito, M., Sanchez-de la Orden, M., Gonzalez-Audicana, M.: Object- and pixel-based analysis for mapping crops and their agroenvironmental associated measures using QuickBird imagery. Comput. Electron. Agric. 68, 207–215 (2009)CrossRefGoogle Scholar
  18. 18.
    Gao, Y., Mas, J.F., Maathius, B.H.P., Xiangmin, Z., van Dijk, P.M.: Comparison of pixel-based and object oriented image classification approaches—a case study of a coal fire area, Wuda, inner Mongolia, China. Int. J. Remote Sens. 27, 4039–4055 (2006)CrossRefGoogle Scholar
  19. 19.
    Gao, Y., Mas, J.F.: A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. In: Proceedings of GEOBIA 2008—Pixels, Objects, Intelligence: Geographic Object-Based Image Analysis for the 21st Century, Calgary, Alberta, 6–7 August 2008Google Scholar
  20. 20.
    Jobin, B., Labrecque, S., Grenier, M., Falardeau, G.: Object-based classification as an alternative approach to the traditional pixel-based classification to identify potential habitat of the Grasshopper Sparrow. Environ. Manag. 41, 20–31 (2008)CrossRefGoogle Scholar
  21. 21.
    Manakos, I., Schneider, T., Ammer, U.: A comparison between the ISODATA and the eCognition classification on basis of field data. In: Proceedings of XIX ISPRS Congress, Amsterdam, 16–22 July 2000Google Scholar
  22. 22.
    Devhari, A., Heck, R.J.: Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer. J. Geogr. Reg. Plan. 2, 86–96 (2009)Google Scholar
  23. 23.
    Guo, X., Huang, X., Zhang, L., Zhang, L., Plaza, A., Benediktsson, J.A.: Support tensor machines for classification of hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 54(6), 3248–3264 (2016)CrossRefGoogle Scholar
  24. 24.
    Watmough, G.R., Palm, C.A., Sullivan, C.: An operational framework for objectbased land use classification of heterogeneous rural landscapes. Int. J. Appl. Earth Obs. Geoinf. 54, 134–144 (2017)CrossRefGoogle Scholar
  25. 25.
    Zhang, C., Selch, D., Cooper, H.: A framework to combine three remotely sensed data sources for vegetation mapping in the central Florida everglades. Wetlands 36(2), 201–213 (2016)CrossRefGoogle Scholar
  26. 26.
    Munoz-Mari, J., Tuia, D., Camps-Valls, G.: Semisupervised classification of remote sensing images with active queries. IEEE Trans. Geosci. Remote Sens. 50(10), 3751–3763 (2012)CrossRefGoogle Scholar
  27. 27.
    Jun, G., Ghosh, J.: Semisupervised learning of hyperspectral data with unknown landcover classes. IEEE Trans. Geosci. Remote Sens. 51(1), 273–282 (2013)CrossRefGoogle Scholar
  28. 28.
    Pal, M.: Extreme-learning-machine-based land cover classification. Int. J. Remote Sens. 30(14), 3835–3841 (2009)CrossRefGoogle Scholar
  29. 29.
    Stankevich, S., Levashenko, V., Zaitseva, E.: Fuzzy decision tree model adaptation to multi- and hyperspectral imagery supervised classification. In: Proceedings of the 9th International Conference on Digital Technologies (DT ‘13), pp. 198–202, Žilina, Slovakia, 2013Google Scholar
  30. 30.
    Schmidt, K.S., Skidmore, A.K., Kloosterman, E.H., Van Oosten, H., Kumar, L., Janssen, J.A.M.: Mapping coastal vegetation using an expert system and hyperspectral imagery. Photogramm. Eng. Remote Sens. 70, 703–715 (2004)CrossRefGoogle Scholar
  31. 31.
    Lucieer, A., Kraak, M.: Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty. Int. J. Geogr. Inf. Sci. 18, 491–512 (2004)CrossRefGoogle Scholar
  32. 32.
    Pham, D.L., Prince, J.L.: An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity in homogeneities. Pattern Recognit. Lett. 20, 57–68 (1999)CrossRefGoogle Scholar
  33. 33.
    Chen, W.J., Giger, M.L., Bick, U.: A fuzzy c-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast enhanced MRI images. Acad. Radiol 13, 63–72 (2006)CrossRefGoogle Scholar
  34. 34.
    Seyedarabi, H., Shamsi, H., Borzabadi, E., Shamsi, M.: A modified fuzzy c-means clustering with spatial information for image segmentation. In: Proceedings of the International Conference on Information and Computer Applications (ICICA 2011), pp. 121–125Google Scholar
  35. 35.
    Shamsi, H., Seyedarabi, H.: A modified fuzzy C-means clustering with spatial information for image segmentation. Int. J. Comput. Theory Eng. 4(5), 762 (2012)CrossRefGoogle Scholar
  36. 36.
    Soufan, O., Kleftogiannis, D., Kalnis, P., Bajic, V.B.: DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS ONE 10(2), e0117988 (2015)CrossRefGoogle Scholar
  37. 37.
    Kumar, M.A., Gopal, M.: Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 36(4), 7535–7543 (2009)CrossRefGoogle Scholar
  38. 38.
    Chen, J., Ji, G.: Weighted least squares twin support vector machines for pattern classification. In: Proceedings of the 2nd International Conference on Computer and Automation Engineering (ICCAE), vol. 2, pp. 242–246 (2010)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science, School of Computer Science and EngineeringBharathiar UniversityCoimbatoreIndia

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