Multiple Classifiers and Graph Cut Methods for Spectral Spatial Classification of Hyperspectral Image

Other literature type English OPEN
Bhushan, D. B. ; Nidamanuri, R. R. (2014)
  • Journal: (issn: 2194-9034, eissn: 2194-9034)
  • Related identifiers: doi: 10.5194/isprsarchives-XL-8-683-2014
  • Subject:
    arxiv: Computer Science::Computer Vision and Pattern Recognition
    acm: ComputingMethodologies_PATTERNRECOGNITION

Hyperspectral image contains fine spectral and spatial resolutions for generating accurate land use and land cover maps. Supervised classification is the one of method used to exploit the information from the hyperspectral image. The traditional supervised classification methods could not be able to overcome the limitations of the hyperspectral image. The multiple classifier system (MCS) has the potential to increase the classification accuracy and reliability of the hyperspectral image. However, the MCS extracts only the spectral information from the hyperspectral image and neglects the spatial contextual information. Incorporating spatial contextual information along with spectral information is necessary to obtain smooth classification maps. Our objective of this paper is to design a methodology to fully exploit the spectral and spatial information from the hyperspectral image for land cover classification using MCS and Graph cut (GC) method. The problem is modelled as the energy minimization problem and solved using -expansion based graph cut method. Experiments are conducted with two hyperspectral images and the result shows that the proposed MCS based graph cut method produces good quality classification map.
  • References (19)
    19 references, page 1 of 2

    Amato, U., Cavalli, R. M., Palombo, A., Pignatti, S. and Santini, F., 2009. Experimental Approach to the Selection of the Components in the Minimum Noise Fraction. IEEE Transactions on Geoscience and Remote Sensing 47(1), pp. 153-160.

    Boykov, Y., Veksler, O. and Zabih, R., 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(11), pp. 1222-1239.

    Briottet, X., Boucher, Y., Dimmeler, A., Malaplate, A., Cini, A., Diani, M., Bekman, H., Schwering, P., Skauli, T., Kasen, I., Renhorn, I., KlaseĀ“n, L., Gilmore, M. and Oxford, D., 2006. Military applications of hyperspectral imagery. In: W. R. Watkins and D. Clement (eds), Proc. SPIE 6239, Targets and Backgrounds XII: Characterization and Representation, International Society for Optics and Photonics, pp. 62390B-62390B-8.

    Camps-Valls, G. and Bruzzone, L., 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43(6), pp. 1351-1362.

    Camps-Valls, G., Tuia, D., Bruzzone, L. and Atli Benediktsson, J., 2014. Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods. IEEE Signal Processing Magazine 31(1), pp. 45-54.

    Cetin, H., Pafford, J. and Mueller, T., 2005. Precision agriculture using hyperspectral remote sensing and GIS. In: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, 2005. RAST 2005., IEEE, pp. 70-77.

    Cloutis, E. A., 1996. Review Article Hyperspectral geological remote sensing: evaluation of analytical techniques. International Journal of Remote Sensing 17(12), pp. 2215-2242.

    Damodaran, B. B. and Nidamanuri, R. R., 2014a. Assessment of the impact of dimensionality reduction methods on information classes and classifiers for hyperspectral image classification by multiple classifier system. Advances in Space Research 53(12), pp. 1720-1734.

    Damodaran, B. B. and Nidamanuri, R. R., 2014b. Dynamic Linear Classifier System for Hyperspectral Image Classification for Land Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(6), pp. 2080- 2093.

    Fabio, R., Giacinto, G. and Vernazza, G., 1997. Comparison and Combination of Statistical and Neural Network Algorithms for Remote-Sensing. In: Neurocomputation in Remote Sensing Data Analysis, Advances in Spatial Science Series, Springer-Verlag, pp. 117-124.

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