A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images

• Autorzy: Maulik U. , Chakraborty D.
• Języki publikacji: EN

Abstrakty

EN

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.

Słowa kluczowe

EN

support vector machine; incremental learning; kernel function; remote sensing imagery; pixel classification; multiple classifier systems

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2010
• Tom: Vol. 101, nr 4
• Strony: 286--304
• Opis fizyczny: Bibliogr. 38 poz., tab., wykr.

Twórcy

autor

Maulik U.

autor

Chakraborty D.

• Department of Electronics and Communication, Murshidabad College of Engineering and Technology, West Bengal University of Technology, Berhampore, Murshidabad, India,

Bibliografia

• [1] Aizerman, M. A., Braveman, E. M., Rozoner, I.I.: Theoretical foundation of the potential function method in pattern recognition learning, Automation and Remote Control, 25, 1964, 821-827.
• [2] Bandyopadhyay, S., Maulik, U.: Non-parametric genetic clustering: comparison of validity indices, IEEE Transactions on Systems, Man and Cybernetics Part-C, 31(1), 2001, 120-125.
• [3] Bandyopadhyay, S., Pal, S. K.: Pixel classification using variable string genetic algorithms with chromosome differentiation, IEEE Transactions on Geoscience and Remote Sensing, 39(2), 2001, 303-308.
• [4] Bandyopadhyay, S., Maulik, U., Mukhopadhyay, A.: Multiobjective genetic clustering for pixel classification in remote sensing imagery, IEEE transactions on Geoscience and Remote Sensing, 45(5), 2007, 1506-1511.
• [5] Bezdek, J. C., Pattern Recognition with Fuzzy Objective Function Algorithms, New York: Plenum, 1981.
• [6] Chen, K. S., Tzeng, Y. C., Chen, C. F., Kao, W. L.: Land cover classification of multispectral imegery using a dynamic learning neural network, Photogrammetric Engineering and Remote Sensing, 25, 1995, 403-408.
• [7] Cohen, J.: A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20, 1960, 37-46.
• [8] Collins,M. J., Dymond, C., Johnson, E. A.: Mapping subalpine forest types using networks of nearest neighbor classifiers, International Journal of Remote Sensing, 25, 2004, 1701-1721.
• [9] Debeir, O., van den Steen, I., Latinne, P., van Ham, P., Wolff, E. :Textural and contextural land cover classification using single and multiple classifier systems, Photogrammetric Engineering and Remote Sensing, 68, 2002, 597-605.
• [10] Defries, R. S., Chan, J. C.: Multiple criteria for evaluating machine learning algorithms for land cover classification from satellite data, Remote Sensing of Environment, 74, 2000, 503-515.
• [11] Foody, G.M.,Mathur, A.: A relative evaluation of multiclass image classification by support vector machine, IEEE Transactions on Geoscience and Remote Sensing, 42, 2004, 1335-1343.
• [12] Foody, G. M., Mathur, A.: Toward intelligent training of supervised image classification: directing training data acquisition for SVM classification, Remote Sensing of Environment, 93, 2004, 107-117.
• [13] Franklin, J., Phinn, S. R.,Woodcock, C. E., Rogan, J.: Rationale and conceptual framework for classification approaches to assess forest resources and properties, in: Remote Sensing of Forest Environments: Concepts and Case Studies (M. A. Wulder and S. E. Franklin, Eds), Boston: Kluwer Academic Publishers, 2003, 279-300.
• [14] Hardin, P. J.: Parametric and nearest neighbor methods for hybrid classification: a comparison of pixel assignment accuracy, Photogrammetric Engineering and Remote Sensing, 60, 1994, 1439-1448.
• [15] Huang, C., Davis, L. S., Townshed, J. R. G.: An assessment of support vectorm machines for land cover classifiaction, International Journal of Remote Sensing, 23, 2002, 725-749.
• [16] Huang, Z., Lees, B. G.: Combining nonparametric models for multisource predictive forest mapping, Photogrammetric Engineering and Remote Sensing, 70, 2004, 415-425.
• [17] Jantke, P. : Types of incremental learning. AAAI Symposium on Training Issues in Incremental learning, March 23-25, 1993, Stanford, CA,
• [18] Kavzoglu, T., Mather, P. M.: The use of backpropagating artificial neural networks in land cover classification, International Journal of Remote Sensing, 24, 2004, 4907-4938.
• [19] Kramer, J. H.: Observation of the earth and its environment, Survey of Missions and Sensors (4th Edition), Berlin, Springer, 2002.
• [20] Lawrence, R., Bunn, A., Powell, S., Zmabon,M.: Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis, Remote Sensing of Environment, 90, 2004, 331-336.
• [21] Li, T. S., Chen, C. Y., Su, C. T.: Comparison of neural and statistical algorithms for supervised classification of multidimensional data, International Journal of Industrial Engineering - Theory, Application and Practice, 10, 2003, 73-81.
• [22] Liu, W., Gopal, S., Woodcock, C. E. Uncertainty and confidence in land cover classification using hybrid classifier approach, Photogrammetric Engineering and Remote Sensing, 70, 2004, 963-972.
• [23] Lo, C. P., Choi, J.: A hybrid approach to urban land use/cover mapping using landsat 7 enhanced thematic mapper plus (ETM + ) images, International Journal of Remote Sensing, 25, 2004, 2687-2700.
• [24] Lu, D., Weng, Q.: Spectral mixture analysis of the urban landscapes in indianapolis with landsat ETM + imagery, Photogrammetric Engineering and Remote Sensing, 70, 2004, 1053-1062.
• [25] Lu, D., Mausel, P., Batistella, M., Morgan, E.: Comparison of land cover classification methods in the Brazillian Amazon basin, Photogrammetric Engineering and Remote Sensing, 70, 2004, 723-731.
• [26] Maulik,U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12), 2002, 1650-1654.
• [27] Mahesh, P., Mather, P. M.: An Assessment of the effectiveness of decision tree methods for land cover classification, Remote Sensing of Environment, 86, 2003, 554-565.
• [28] Osuna, E., Freund, R., Girosi, F.: Training Support vector Machine: an application to face detection, IEEE Conference on Computer Vision and Pattern Recognition, 1997, 130-136.
• [29] Peddle, D. R., Foody, G. M., Zhang, A., Franklin, S., LeDrew, E. F.:Multisource image classification II: an empirical comparison of evidential reasoning and neural network approaches, Canadian Journal of Remote Sensing, 20, 1994, 396-407.
• [30] Pontil, M., Verri, A.: Support vector machines for 3-D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 1998, 637-646.
• [31] Rogan, J., Roberts, D. A.: A comparison of methods for monitoring multitemporal vegetation change using thematic mapper imagery, Remote Sensing of Environment, 80, 2002, 143-156.
• [32] Scholkopf, B., Kah-Kay, C., Burges, C. J., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with gaussian kernels to radial basis fuction classifiers, IEEE Transactions on Signal Processings, 45, 1997, 2758-2765.
• [33] Steele, B. M.: Combining multiple classifiers: an application using special and remotely sensed information for land cover type mapping, Remote Sensing of Environment, 74, 2000, 545-556.
• [34] Tou, J. T., Gonzalez, R. C.: Pattern Recognition Principles, Addison-Wesley, 1974.
• [35] Tso, B., Mather, P. M. Classification Methods for Remotely Sensed Data, New York: Taylor and Francis Inc., 2001.
• [36] Vapnik, V.: Statistical Learning Theory, New York,Wiley, 1998.
• [37] Wan, V., Campbell,W. M.: Support vector machines for speaker verification and identification, in: Proceedings IEEE Workshop on Neural Networks for Signal Processings, Sydney, Australia, 2000, 775-784.
• [38] Xie, X. L., Beni, G.: A validity measure for fuzzy clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, 13, 1991, 841-847.