Novel Classification and Segmentation Techniques with Application to Remotely Sensed Images

  • B. Uma Shankar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4400)


The article deals with some new results of investigation, both theoretical and experimental, in the area of image classification and segmentation of remotely sensed images. The article has mainly four parts. Supervised classification is considered in the first part. The remaining three parts address the problem of unsupervised classification (segmentation). The effectiveness of an active support vector classifier that requires reduced number of additional labeled data for improved learning is demonstrated in the first part. Usefulness of various fuzzy thresholding techniques for segmentation of remote sensing images is demonstrated in the second part. A quantitative index of measuring the quality of classification/ segmentation in terms of homogeneity of regions is introduced in this regard. Rough entropy (in granular computing framework) of images is defined and used for segmentation in the third part. In the fourth part a homogeneous region in an image is defined as a union of homogeneous line segments for image segmentation. Here Hough transform is used to generate these line segments. Comparative study is also made with related techniques.


Active learning Support vector machine  Fuzzy sets Fuzzy entropy Fuzzy correlation Rough sets Rough entropy Granular computing Soft-computing Hough transform  Remotely sensed images 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Osteaux, M., Meirleir, K.D., Shahabpour, M. (eds.): Magnetic Resonance Imaging and Spectroscopy in sports medicine. Springer, Berlin (1991)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education, Inc., Singapore (2002)Google Scholar
  3. 3.
    Hall, E.L.: Computer Image Processing and Recognition. Academic Press, New York (1979)zbMATHGoogle Scholar
  4. 4.
    Marr, D.: Vision. Freeman, San Fransicsco (1982)Google Scholar
  5. 5.
    Rosenfeld, A., Kak, A.C.: Digital Picture Processing, vol. I & II. Academic Press, New York (1982)Google Scholar
  6. 6.
    Horowitz, S.L., Pavlidis, T.: Picture segmentation by directed split and merge procedure. In: Proc. 2nd Int. Joint Conf. Pattern Recognition, pp. 424–433 (1974)Google Scholar
  7. 7.
    Perez, A., Gonzalez, R.C.: An iterative thresholding algorithm for image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 9(6), 742–751 (1987)Google Scholar
  8. 8.
    Pal, N.R., Pal, S.K.: Image model, poisson distribution and object extraction. Int. J. Pattern Recognition and Artificial Intelligence 5, 459–483 (1991)Google Scholar
  9. 9.
    Besl, P.J., Jain, R.C.: Segmentation through variable order surface fitting. IEEE Trans. Pattern Analysis and Machine Intelligence 10(2), 167–192 (1988)Google Scholar
  10. 10.
    Taxt, T., Flynn, P.J., Jain, A.K.: Segmentation of document images. IEEE Trans. Pattern Analysis and Machine Intelligence 11(12), 1322–1329 (1989)Google Scholar
  11. 11.
    Chow, C., Kaneko, T.: Automatic boundary detection of the left ventricle from cineangiograms. Computers and Biomedical Research 5, 388–341 (1972)Google Scholar
  12. 12.
    Nakagawa, Y., Rosenfeld, A.: Some experiments on variable thresholding. Pattern Recognition 11, 191–204 (1979)Google Scholar
  13. 13.
    Yanowitz, S.D., Bruckstein, A.M.: A new method for image segmentation. Computer Vision, Graphics and Image Processing 46, 82–95 (1989)Google Scholar
  14. 14.
    Mardia, K.V., Hainsworth, T.J.: A spatial thresholding method for image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 10(6), 919–927 (1988)Google Scholar
  15. 15.
    Ridler, T.W., Calvard, S.: Picture thresholding using an iterative selection method. IEEE Trans. System, Man and Cybernetics 8, 630–632 (1978)Google Scholar
  16. 16.
    Lloyd, D.E.: Automatic target classification using moment invariants of image shapes. Report RAE IDN AW126, Farnborough, UK (1985)Google Scholar
  17. 17.
    Otsu, N.: A threshold selection method from grey-level histograms. IEEE Trans. Systems, Man and Cybernetics 9, 62–66 (1979)Google Scholar
  18. 18.
    Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19(1), 41–47 (1986)Google Scholar
  19. 19.
    Pal, N.R., Bhandari, D.: On object-background classification. Int. J. Systems Science 23, 1903–1920 (1992)zbMATHMathSciNetGoogle Scholar
  20. 20.
    Pun, T.: A new method for gray level picture thresholding using the entropy of the histogram. Signal Processing 2, 223–237 (1980)Google Scholar
  21. 21.
    Kapur, J.N., Sahoo, P.K., Wong, A.K.C.: A new method for gray level picture thresholding using the entropy of histogram. Computer Vision, Graphics and Image Processing 29, 273–285 (1985)Google Scholar
  22. 22.
    Wong, A.K.C., Sahoo, P.K.: A gray level threshold selection method based on maximum entropy principle. IEEE Trans. Systems, Man and Cybernetics 19, 866–871 (1989)Google Scholar
  23. 23.
    Levine, M.D., Nazif, A.M.: Dynamic measurement of computer generated image segmentation. IEEE Trans. Pattern Analysis and Machine Intelligence 7, 155–164 (1985)Google Scholar
  24. 24.
    Weszka, J.S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Trans. Systems, Man and Cybernetics 8, 622–629 (1978)Google Scholar
  25. 25.
    Deravi, F., Pal, S.K.: Gray level thresholding using second order statistics. Pattern Recognition Letters 1, 417–422 (1983)Google Scholar
  26. 26.
    Chanda, B., Chaudhuri, B.B., Majumder, D.D.: On image enhancement and threshold selection using the gray level co-occurrence matrix. Pattern Recognition Letters 3, 243–251 (1985)Google Scholar
  27. 27.
    Pal, S.K., Pal, N.R.: Segmentation based on measures of contrast, homogeneity, and region size. IEEE Trans. Systems, Man and Cybernetics 17, 857–868 (1987)Google Scholar
  28. 28.
    Pal, N.R., Pal, S.K.: Entropic thresholding. Signal Processing 106, 97–108 (1989)Google Scholar
  29. 29.
    Abutaleb, A.S.: Automatic thresholding of gray level pictures using two-dimensional entropy. Computer Vision, Graphics and Image Processing 47, 22–32 (1989)Google Scholar
  30. 30.
    Peleg, S.: A new probabilistic relaxation scheme. IEEE Trans. Pattern Analysis and Machine Intelligence 2, 362–369 (1980)zbMATHGoogle Scholar
  31. 31.
    Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Trans. Systems, Man and Cybernetics 6, 420–433 (1976)zbMATHMathSciNetGoogle Scholar
  32. 32.
    Asker, M., Derin, H.: A recursive algorithm for the Bayes solution of the smoothing problem. IEEE Trans. Automatic Control 26, 558–561 (1981)Google Scholar
  33. 33.
    Derin, H., et al.: Bayes smoothing algorithms for segmentation of binary images modeled by markov random fields. IEEE Trans. Pattern Analysis and Machine Intelligence 6, 707–720 (1984)zbMATHGoogle Scholar
  34. 34.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. Pattern Analysis and Machine Intelligence 6, 707–720 (1984)zbMATHGoogle Scholar
  35. 35.
    Kohonen, T.: Self-organization and Associative Memory. Springer, New York (1989)Google Scholar
  36. 36.
    Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, New York (1989)zbMATHGoogle Scholar
  37. 37.
    Babaguchi, N., et al.: Connectionist model binarization. In: Proc. 10th ICPR, pp. 51–56 (1990)Google Scholar
  38. 38.
    Blanz, W.E., Gish, S.L.: A connectionist classifier architecture applied to image segmentation. In: Proc. 10th ICPR, pp. 272–277 (1990)Google Scholar
  39. 39.
    Chen, C.T., Tsao, E.C., Lin, W.C.: Medical image segmentation by a constraint satisfaction neural network. IEEE Trans. Nuclear Science 38(2), 678–686 (1991)Google Scholar
  40. 40.
    Ghosh, A., Pal, N.R., Pal, S.K.: Image segmentation using neural networks. Biological Cybernetics 66(2), 151–158 (1991)zbMATHGoogle Scholar
  41. 41.
    Ghosh, A., Pal, N.R., Pal, S.K.: Neural network, Gibbs distribution and object extraction. In: Vidyasagar, M., Trivedi, M. (eds.) Intelligent Robotics, New Delhi, pp. 95–106. McGraw-Hill, New York (1991)Google Scholar
  42. 42.
    Ghosh, A., Pal, N.R., Pal, S.K.: Object background classification using hopfield type neural network. Int. J. Pattern Recognition and Artificial Intelligence 6(5), 989–1008 (1992)Google Scholar
  43. 43.
    Kuntimad, G., Ranganath, H.S.: Perfect image segmentation using pulse coupled neural networks. IEEE Trans. Neural Networks 10(3), 591–598 (1999)Google Scholar
  44. 44.
    Manjunath, B.S., Simchony, T., Chellappa, R.: Stochastic and deterministic network for texture segmentation. IEEE Trans. Acoustics Speech Signal Processing 38, 1039–1049 (1990)Google Scholar
  45. 45.
    Eckhorn, R., et al.: Feature linking via synchronization among distributed assemblies: Simulation of results from cat cortex. Neural Computation 2(3), 293–307 (1990)Google Scholar
  46. 46.
    Ghosh, S., Ghosh, A.: A GA-FUZZY approach to evolve hopfield type optimum networks for object extraction. In: Pal, N.R., Sugeno, M. (eds.) AFSS 2002. LNCS (LNAI), vol. 2275, pp. 444–449. Springer, Heidelberg (2002)Google Scholar
  47. 47.
    Pal, S.K., De, S., Ghosh, A.: Designing hopfield type networks using genetic algorithms and its comparison with simulated annealing. Int. J. Pattern Recognition and Artificial Intelligence 11(3), 447–461 (1997)Google Scholar
  48. 48.
    Jiang, Y., Zhou, Z.: SOM ensemble-based image segmentation. Neural Processing Letters 20(3), 171–178 (2004)Google Scholar
  49. 49.
    Gonzalez, R.C., Wintz, P.: Digital Image Processing. Addison-Wesley, Reading (1987)Google Scholar
  50. 50.
    Davis, L.S.: A survey of edge detection techniques. Computer Graphics and Image Processing 4(3), 248–270 (1975)Google Scholar
  51. 51.
    Kundu, M.K., Pal, S.K.: Thresholding for edge detection using human psychovisual phenomena. Pattern Recognition Letters 4, 433–441 (1986)Google Scholar
  52. 52.
    Haddon, J.F.: Generalized threshold selection for edge detection. Pattern Recognition 21, 195–203 (1988)Google Scholar
  53. 53.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)zbMATHMathSciNetGoogle Scholar
  54. 54.
    Prewitt, J.M.S.: Object enhancement and extraction. In: Lipkin, B.S., Rosenfeld, A. (eds.) Picture Processing and Psycho-Pictorics, pp. 75–149. Academic Press, New York (1970)Google Scholar
  55. 55.
    Pal, S.K., King, R.A.: Image enhancement using fuzzy sets. Electronic Letters 16, 376–378 (1980)Google Scholar
  56. 56.
    Pal, S.K., Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recognition Letters 7(2), 77–86 (1988)zbMATHGoogle Scholar
  57. 57.
    Murthy, C.A., Pal, S.K.: Fuzzy thresholding: Mathematical framework, bound functions and weighted moving average technique. Pattern Recognition Letters 11, 197–206 (1990)zbMATHGoogle Scholar
  58. 58.
    Pal, S.K., Dasgupta, A.: Spectral fuzzy sets and soft thresholding. Information Sciences 65, 65–97 (1992)zbMATHMathSciNetGoogle Scholar
  59. 59.
    Xie, W.X., Bedrosian, S.D.: Experimentally driven fuzzy membership function for gray level images. J. Franklin Institute 325, 154–164 (1988)MathSciNetGoogle Scholar
  60. 60.
    Tobias, O.J., Seara, R.: Image segmentation by histogram thresholding using fuzzy sets. IEEE Trans. Image Processing 12(11), 1457–1465 (2002)Google Scholar
  61. 61.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  62. 62.
    Hall, L.O., et al.: A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans. Neural Networks 3(5), 672–681 (1992)Google Scholar
  63. 63.
    Huntsberger, T.L., Jacobs, C.L., Cannon, R.L.: Iterative fuzzy image segmentation. Pattern Recognition 18, 131–138 (1985)Google Scholar
  64. 64.
    Trivedi, M.M., Bezdek, J.C.: Low-level segmentation of aerial images with fuzzy clustering. IEEE Trans. Systems, Man and Cybernetics 16(4), 589–598 (1986)Google Scholar
  65. 65.
    Cannon, R.L., Dave, J.V., Bezdek, J.C.: Efficient implementation of fuzzy c-means clustering algorithms. IEEE Trans. Pattern Analysis and Machine Intelligence 8(2), 248–255 (1986)zbMATHGoogle Scholar
  66. 66.
    Keller, J.M., Carpenter, C.L.: Image segmentation in presence of uncertainty. Int. J. Intelligent Systems 5, 193–208 (1990)zbMATHGoogle Scholar
  67. 67.
    Couprie, M., Najman, L., Bertrand, G.: Quasi-linear algorithms for the topological watershed. J. Mathematical Imaging and Vision 22(2-3), 231–249 (2005)MathSciNetGoogle Scholar
  68. 68.
    Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)zbMATHGoogle Scholar
  69. 69.
    Patras, I., Lagendijk, R.L., Hendriks, E.A.: Video segmentation by MAP labeling of watershed segments. IEEE Trans. Pattern Analysis and Machine Intelligence 23(3), 326–332 (2001), doi:10.1109/34.910886Google Scholar
  70. 70.
    Bhanu, B., Fonder, S.: Functional template-based SAR image segmentation. Pattern Recognition 37(1), 61–77 (2004)Google Scholar
  71. 71.
    Bhanu, B., Lee, S.: Genetic Learning for Adaptive Image Segmentation. Kluwer Academic Publishers, Norwell (1994)zbMATHGoogle Scholar
  72. 72.
    Acharyya, M., De, R.K., Kundu, M.K.: Segmentation of remotely sensed images using wavelets features and their evaluation in soft computing framework. IEEE Trans. Geoscience and Remote Sensing 41(12), 2900–2905 (2003)Google Scholar
  73. 73.
    Heiler, M., Schnörr, C.: Natural image statistics for natural image segmentation. Int. J. Computer Vision 63(1), 5–19 (2005)Google Scholar
  74. 74.
    Ho, S., Bullitt, E., Gerig, G.: Level-set evolution with region competition: Automatic 3-D segmentation of brain tumors. In: ICPR ’02: Proc. 16th Int. Conf. on Pattern Recognition, vol. 1, Washington, DC, USA, pp. 532–535. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  75. 75.
    Kervrann, C., Trubuil, A.: Optimal level curves and global minimizers of cost functionals in image segmentation. J. Mathematical Imaging 17(2), 153–174 (2002)zbMATHMathSciNetGoogle Scholar
  76. 76.
    Lin, P., et al.: Statistical model based on level set method for image segmentation. In: Das, G., Gulati, V.P. (eds.) CIT 2004. LNCS, vol. 3356, pp. 143–148. Springer, Heidelberg (2004)Google Scholar
  77. 77.
    Pal, S.K., Ghosh, A., Kundu, M.K.: Soft Computing for Image Processing. Physica, Heidelberg (2000)zbMATHGoogle Scholar
  78. 78.
    Zadeh, L.A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 90, 111–127 (1997)zbMATHMathSciNetGoogle Scholar
  79. 79.
    Ghosh, A., Pal, N.R., Pal, S.K.: Self-organization for object extraction using multilayer neural network and fuzziness measures. IEEE Trans. Fuzzy Systems 1(1), 54–68 (1993)Google Scholar
  80. 80.
    Ohlander, R.B.: Analysis of natural scenes. PhD thesis, Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania (1975)Google Scholar
  81. 81.
    Overheim, R.D., Wagner, D.L.: Light and Color. Wiley, New York (1982)Google Scholar
  82. 82.
    Cheng, H.D., et al.: Color image segmentation: Advances and prospects. Pattern Recognition 34(12), 2259–2281 (2001)zbMATHGoogle Scholar
  83. 83.
    Naik, S.K., Murthy, C.A.: Standardization of edge magnitude in color images. IEEE Trans. Image Processing, Communicated (2005)Google Scholar
  84. 84.
    Naik, S.K., Murthy, C.A.: Distinct multi-colored region descriptors for object recognition. IEEE Trans. Pattern Analysis and Machine Intelligence, Communicated (2005)Google Scholar
  85. 85.
    Levine, M.D., Nazif, A.M.: An experimental rule based system for testing low level segmentation strategy. In: Uhr, L., Preston, K. (eds.) Multi-Computer Architectures and Image Processing: Algorithms and Programs, Academic Press, New York (1982), Also available as Report No. 81-6, Department of Electrical Engineering, McGill University, June 1981 (1982)Google Scholar
  86. 86.
    Lim, Y.W., Lee, S.U.: On the color image segmentation algorithm based on the thresholding and fuzzy c-means techniques. Pattern Recognition 23, 935–952 (1990)Google Scholar
  87. 87.
    Pal, N.R., Bhandari, D.: Object background classification: Some new techniques. Signal Processing 33(2), 139–158 (1993)zbMATHGoogle Scholar
  88. 88.
    Brink, A.B.: Gray level thresholding of images using a correlation criterion. Pattern Recognition Letters 9, 335–341 (1989)zbMATHGoogle Scholar
  89. 89.
    Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(6), 1277–1294 (1993)Google Scholar
  90. 90.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. J. Electronic Imaging 13(1), 146–165 (2004)Google Scholar
  91. 91.
    Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks - a review. Pattern Recognition 35(10), 2279–2301 (2002)zbMATHGoogle Scholar
  92. 92.
    Freixenet, J., et al.: Yet another survey on image segmentation: Region and boundary information integration. In: Heyden, A., et al. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 408–422. Springer, Heidelberg (2002)Google Scholar
  93. 93.
    Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recognition 13, 3–16 (1981)MathSciNetGoogle Scholar
  94. 94.
    Haralick, R.M., Shapiro, L.G.: Survey, image segmentation techniques. Computer Vision, Graphics and Image Processing 29, 100–132 (1985)Google Scholar
  95. 95.
    Karmakar, G.C., Dooley, L., Syed, M.R.: Review of fuzzy image segmentation techniques. In: Design and management of multimedia information systems: Opportunities and challenges, Hershey, PA, USA, pp. 282–314 (2001)Google Scholar
  96. 96.
    Sahoo, P.K., et al.: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41(2), 233–260 (1988)Google Scholar
  97. 97.
    Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)Google Scholar
  98. 98.
    Mather, P.M.: Computer Processing of Remotely-Sensed Images: An Introduction. John Wiley & Sons, Chichester (1999)Google Scholar
  99. 99.
    Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction, 3rd edn. Springer, Heidelberg (1999)Google Scholar
  100. 100.
    Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 2nd edn. Academic Press, San Diego (1997)Google Scholar
  101. 101.
    Thiruvengadachari, S., Kalpana, A.R.: IRS Data Users Handbook (Revision 1), NRSA Data Centre, Dept. of Space, Govt. of India (1989)Google Scholar
  102. 102.
    Swain, P.H., Davis, S.M.: Remote Sensing: The Quantitative Approach. McGraw Hill Inc., New York (1978)Google Scholar
  103. 103.
    Bauer, M.E., Cipra, J.: Identification of agricultural crops by computer processing of ERTS MSS data. In: Symposium on Significant Results Obtained from ERTS-1, NASA Document no. SP-327, pp. 205–212 Washington, DC (1973)Google Scholar
  104. 104.
    Kettig, R.L., Landgrebe, D.A.: Computer classification of remotely sensed multispectral image data by extraction and classification of homogeneous objects. IEEE Trans. Geoscience Electronics 14(1), 19–26 (1976)Google Scholar
  105. 105.
    Lee, C., Landgrebe, D.A.: Fast multistage likelihood classification. IEEE Trans. Geoscience and Remote Sensing 29(4), 509–517 (1991)Google Scholar
  106. 106.
    Sun, W., et al.: Information fusion for rural land-use classification with high-resolution satellite imagery. IEEE Trans. Geoscience and Remote Sensing 41(4), 883–890 (2003)Google Scholar
  107. 107.
    Bandyopadhyay, S., Pal, S.K.: Pixel classification using variable string genetic algorithms with chromosome differentiation. IEEE Trans. Geoscience and Remote Sensing 29(2), 303–308 (2001)Google Scholar
  108. 108.
    Bischof, H., Schneider, W., Pinz, A.J.: Multispectral classification of landsat-images using neural networks. IEEE Trans. Geoscience and Remote Sensing 30(3), 482–490 (1992)Google Scholar
  109. 109.
    Erbek, S.F., Ozkan, C., Taberner, M.: Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. Int. J. Remote Sensing 25(9), 1733–1748 (2004)Google Scholar
  110. 110.
    Pal, M., Mather, P.M.: Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems 20(7), 1215–1225 (2004), doi:10.1016/j.future.2003.11.011Google Scholar
  111. 111.
    Murthy, C.A., et al.: IRS image segmentation: Minimum distance classifier approach. In: Proc. 11th ICPR, The Hague, The Netherlands, August-September 1992, pp. 781–784. IEEE Computer Society Press, Los Alamitos (1992)Google Scholar
  112. 112.
    Wacker, A.G., Landgrebe, D.A.: Minimum distance classification in remote sensing. In: First Canadian Symposium on Remote Sensing, Ottawa, Canada, Also available as LARS Technical Note 030772 (25 pages), Purdue University, Lafayette Indiana (1972)Google Scholar
  113. 113.
    Khazenie, N., Crawford, M.M.: Spatial-temporal autocorrelated model for contextual classification. IEEE Trans. Geoscience and Remote Sensing 28(4), 529–539 (1990)Google Scholar
  114. 114.
    Li, F., Peng, J.: Double random field models for remote sensing image segmentation. Pattern Recognition Letters 25(1), 129–139 (2004), doi:10.1016/j.patrec.2003.09.006MathSciNetGoogle Scholar
  115. 115.
    Jhung, Y., Swain, P.H.: Bayesian contextual classification based on modified M-estimates and markov random fields. IEEE Trans. Geoscience and Remote Sensing 34(1), 67–75 (1996)Google Scholar
  116. 116.
    Gong, P., Howarth, P.J.: Performance analysis of probabilistic relaxation methods for land-cover classification. Remote Sensing of Environment 30, 33–42 (1989)Google Scholar
  117. 117.
    Richards, J.A., Landgrebe, D.A., Swain, P.H.: Pixel labeling by supervised probabilistic relaxation. IEEE Trans. Pattern Analysis and Machine Intelligence 3(2), 188–191 (1981)Google Scholar
  118. 118.
    Solberg, A.H.S., Taxt, T., Jain, A.K.: A markov random field model for classification of multisource satellite imagery. IEEE Trans. Geoscience and Remote Sensing 34(1), 100–113 (1996)Google Scholar
  119. 119.
    Swain, P.H., Hauska, H.: The decision tree classifier: Design and potential. IEEE Trans. Geoscience Electronics 15, 142–147 (1977)Google Scholar
  120. 120.
    Pal, M., Mather, P.M.: An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment 86, 554–565 (2003)Google Scholar
  121. 121.
    Parui, S.K., et al.: Unsupervised classification of Indian remote sensing satellite imagery. In: Proc. ICAPRDT, Indian Statistical Institute, Calcutta, December 1993, pp. 68–74 (1993)Google Scholar
  122. 122.
    Ho, S., Lee, K.: Design and analysis of an efficient evolutionary image segmentation algorithm. J. VLSI Signal Processing Systems 35(1), 29–42 (2003)Google Scholar
  123. 123.
    Sahasrabudhe, S.C., Dasgupta, K.S.: A valley-seeking threshold selection technique. In: Shapiro, L., Rosenfeld, A. (eds.) Computer Vision and Image Processing: CVIP92, pp. 55–65. Academic Press, Boston (1992)Google Scholar
  124. 124.
    Laprade, R.H.: Split-and-merge segmentation of aerial photographs. Computer Vision, Graphics and Image Processing 44(1), 77–86 (1988)Google Scholar
  125. 125.
    Baraldi, A., Parmiggiani, F.: Single linkage region growing algorithms based on the vector degree of match. IEEE Trans. Geoscience and Remote Sensing 34(1), 137–148 (1996)Google Scholar
  126. 126.
    Pal, S.K., Mitra, P.: Multispectral image segmentation using the rough-set-initialized EM algorithm. IEEE Trans. Geoscience and Remote Sensing 40(11), 2495 (2002)Google Scholar
  127. 127.
    Shah, C.A., et al.: ICA mixture model based unsupervised classification of hyperspectral imagery. In: Proc. 31st Applied Image Pattern Recognition Workshop (AIPR 2002), From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation, Washington, DC, USA, pp. 29–35. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  128. 128.
    Myers, V.I.: Remote sensing applications in agriculture. In: Colwell, J.E., Colwell, R.N. (eds.) Manual of Remote Sensing, pp. 2111–2228. American Society of Photogrammetry, Falls Church (1983)Google Scholar
  129. 129.
    Vaiopoulos, D., Skianis, G.A., Nikolakopoulos, K.: The contribution of probability theory in assessing the efficiency of two frequently used vegetation indices. Int. J. Remote Sensing 25(20), 4219–4236 (2004)Google Scholar
  130. 130.
    McFeeters, S.K.: The use of the normalized difference water index in the delineation of open water features. Int. J. Remote Sensing 17(7), 1425–1432 (1996)Google Scholar
  131. 131.
    Wang, F.: Fuzzy supervised classification of remote sensing images. IEEE Trans. Geoscience and Remote Sensing 28(2), 194–201 (1990)Google Scholar
  132. 132.
    Melgani, F., Hashemy, B.A.R.A., Taha, S.M.R.: An explicit fuzzy supervised classification method for multispectral remote sensing images. IEEE Trans. Geoscience and Remote Sensing 38(1), 287–295 (2000)Google Scholar
  133. 133.
    Mandal, D.P., Murthy, C.A., Pal, S.K.: Utility of multiple choices is detecting ill-defined roadlike structures. Fuzzy Sets and Systems 64, 213–228 (1994)Google Scholar
  134. 134.
    Pal, S.K., Murthy, C.A., Shankar, B.U.: Pixel classification in remotely sensed images using shape estimation with fuzzy sets. In: Mardia, K.V., Gill, C.A., Dryden, I.L. (eds.) Proc. Image Fusion and Shape Variability Techniques, Leeds, U.K., July 1996, pp. 141–145 (1996)Google Scholar
  135. 135.
    Cannon, R.L., et al.: Segmentation of a thematic mapper image using the fuzzy c-means clustering algorithm. IEEE Trans. Geoscience and Remote Sensing 24, 400–408 (1986)Google Scholar
  136. 136.
    Shankar, B.U., Pal, N.R.: FFCM: An effective approach for large data sets. In: Proc. 3rd Int. Conf. on Fuzzy Logic, Neural nets and Soft Computing, Iizuka, Japan, August 1994, pp. 331–332 (1994)Google Scholar
  137. 137.
    Maulik, U., Bandyopadhyay, S.: Fuzzy partitioning using real coded variable length genetic algorithm for pixel classification. IEEE Trans. Geosciences and Remote Sensing 41(5), 1075–1081 (2003)Google Scholar
  138. 138.
    Mecocci, A., et al.: Texture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering. In: Franceschetti, G., et al. (eds.) Synthetic Aperture Radar and Passive Microwave Sensing, November 1995. Proc. SPIE, vol. 2584, pp. 142–151 (1995)Google Scholar
  139. 139.
    Lorette, A., Descombes, X., Zerubia, J.: Texture analysis through a markovian modelling and fuzzy classification: Application to urban area extraction from satellite images. Int. J. Computer Vision 36(3), 221–236 (2000)Google Scholar
  140. 140.
    Baraldi, A., Parmiggiani, F.: Neural network for unsupervised categorization of multivalued input patterns: An application to satellite image clustering. IEEE Trans. Geoscience and Remote Sensing 33(2), 305–316 (1990)Google Scholar
  141. 141.
    Benediktsson, J.A., Swain, P.H., Ersoy, O.K.: Neural network approach versus statistical methods in classification of multisource remote sensing data. IEEE Trans. Geoscience and Remote Sensing 28(4), 540–552 (1990)Google Scholar
  142. 142.
    Benediktsson, J.A., Swain, P.H., Ersoy, O.K.: Conjugate - gradient neural networks in classification of multisource and very-high-dimension remote sensing data. Int. J. Remote Sensing 14, 2883–2903 (1993)Google Scholar
  143. 143.
    Decatur, S.E.: Application of neural network to terrain classification. In: Proc. IJCNN’89, vol. I, Washington DC, USA, pp. 283–288 (1989)Google Scholar
  144. 144.
    Lee, J., et al.: A neural network approach to cloud classification. IEEE Trans. Geoscience and Remote Sensing 28(5), 846–855 (1990)Google Scholar
  145. 145.
    Liu, Z., et al.: Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification. Future Generation Computer Systems 20(7), 1119–1129 (2004), doi:10.1016/j.future.2003.11.024Google Scholar
  146. 146.
    Villmann, T., Merényi, E.: Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis. In: Self-Organizing neural networks: Recent advances and applications, pp. 121–144. Springer, New York (2002)Google Scholar
  147. 147.
    Villmann, T., Merényi, E., Hammer, B.: Neural maps in remote sensing image analysis. Neural Networks 16(3-4), 389–403 (2003)Google Scholar
  148. 148.
    Xue, X., et al.: A new method of SAR image segmentation based on neural network. In: ICCIMA ’03: Proc. 5th Int. Conf. on Computational Intelligence and Multimedia Applications, Washington, DC, USA, pp. 149–153. IEEE Computer Society Press, Los Alamitos (2003)Google Scholar
  149. 149.
    Mandal, D.P., Murthy, C.A., Pal, S.K.: Analysis of IRS imagery for detecting man-made objects with a multivalued recognition system. IEEE Trans. Systems, Man and Cybernetics, Part A 26(2), 241–247 (1996)Google Scholar
  150. 150.
    Ton, J.: A Knowledge Based Approach for Landsat Image Interpretation. PhD thesis, Michigan State University, Michigan, USA (1988)Google Scholar
  151. 151.
    Ton, J., Sticklen, J., Jain, A.K.: Knowledge-based segmentation of landsat images. IEEE Trans. Geoscience and Remote Sensing 29(2), 222–232 (1991)Google Scholar
  152. 152.
    Pal, S.K., Bandyopadhyay, S., Murthy, C.A.: Genetic classifiers and remotely sensed images: Comparison with standard method. Int. J. Remote Sensing 22(13), 2445–2569 (2001)Google Scholar
  153. 153.
    Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35(2), 1197–1208 (2002)zbMATHGoogle Scholar
  154. 154.
    Xie, X.L., Beni, G.: A validity measure for fuzzy clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 13(8), 841–847 (1991), doi:10.1109/34.85677Google Scholar
  155. 155.
    Brown, M., Lewis, H.G., Gunn, S.R.: Linear spectral mixture models and support vector machine for remote sensing. IEEE Trans. Geoscience and Remote Sensing 38(5), 2346–2360 (2000)Google Scholar
  156. 156.
    Huang, C., Davis, L.S., Townshend, J.R.G.: An assessment of support vector machine for land cover classification. Int. J. Remote Sensing 23(4), 725–749 (2002)Google Scholar
  157. 157.
    Varshney, P.K., Arora, M.K.: Advanced Image Processing Techniques for Remote Sensed Hyperspectral Data. Springer, Heidelberg (2004)Google Scholar
  158. 158.
    Kundu, M.K., Acharyya, M.: M-band wavelets: Application to texture segmentation for real life images analysis. Int. J. Wavelets, Multiresolution and information Processing 1(1), 115–149 (2003)zbMATHGoogle Scholar
  159. 159.
    Lindsay, R.W., Percival, D.B., Rothrock, D.A.: The discrete wavelet transform and the scale analysis of the surface properties of sea ice. IEEE Trans. Geoscience and Remote Sensing 34(3), 771–787 (1996)Google Scholar
  160. 160.
    Niedermeier, A., Romaneessen, E., Lehner, S.: Detection of coastlines in SAR images using wavelet methods. IEEE Trans. Geoscience and Remote Sensing 36(5), 2270–2281 (2000)Google Scholar
  161. 161.
    Parui, S.K., et al.: A parallel algorithm for detection of linear structures in satellite images. Pattern Recognition Letters 12, 765–770 (1991)Google Scholar
  162. 162.
    Hu, J., Sakoda, B., Pavlidis, T.: Interactive road finding for aerial images. In: Proc. IEEE Workshop on Applications of Computer Vision, pp. 56–63. IEEE Computer Society Press, Los Alamitos (1992)Google Scholar
  163. 163.
    Zlotnick, A., Carnine Jr., P.D.: Finding road seeds in aerial images. CVGIP: Image Understanding 57(2), 243–260 (1993)Google Scholar
  164. 164.
    Barzohar, M., Cooper, D.B.: Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. In: IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, pp. 459–464. IEEE Computer Society Press, Los Alamitos (1993)Google Scholar
  165. 165.
    Geman, D., Jedynak, B.: An active testing model for tracking roads in satellite images. IEEE Trans. Pattern Analysis and Machine Intelligence 18(1), 1–14 (1996), doi:10.1109/34.476006Google Scholar
  166. 166.
    Gruen, A., Li, H.: Semi-automatic linear feature extraction by dynamic programming and LSB-Snakes. Photogrammetric Engineering and Remote Sensing 63(8), 985–995 (1997)Google Scholar
  167. 167.
    Park, S.R., Kim, T.: Semi-automatic road extraction algorithm from IKONOS images using template matching. In: Proc. 22nd Asian Conf. on remote Sensing, pp. 1209–1213 (2001)Google Scholar
  168. 168.
    Stoica, R., Descombes, X., Zerubia, J.: A Gibbs point process for road extraction from remotely sensed images. Int. J. Computer Vision 57(2), 121–136 (2004)Google Scholar
  169. 169.
    Mena, J.B.: State of the art on automatic road extraction for GIS update: A novel classification. Pattern Recognition Letters 24(16), 3037–3058 (2003)Google Scholar
  170. 170.
    Mitra, P., Shankar, B.U., Pal, S.K.: Active support vector machines for pixel classification in remote sensing images. In: Proc. 1st Indian International Conference on Artificial Intelligence, IICAI-03, pp. 543–553 (2003)Google Scholar
  171. 171.
    Mitra, P., Shankar, B.U., Pal, S.K.: Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognition Letters 25(9), 1067–1074 (2004)Google Scholar
  172. 172.
    Pal, S.K., Ghosh, A., Shankar, B.U.: Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation. Int. J. Remote Sensing 21(11), 2269–2300 (2000)Google Scholar
  173. 173.
    Shankar, B.U., Ghosh, A., Pal, S.K.: On fuzzy thresholding of remotely sensed images. In: Pal, S.K., Ghosh, A., Kundu, M.K. (eds.) Soft Computing for image processing, pp. 130–161. Physica, Heidelberg (2000)Google Scholar
  174. 174.
    Pal, S.K., Shankar, B.U., Mitra, P.: Granular computing, rough entropy and object extraction. Pattern Recognition Letters 26(16), 2509–2517 (2005), doi:10.1016/j.patrec.2005.05.007Google Scholar
  175. 175.
    Shankar, B.U., Murthy, C.A., Pal, S.K.: A new gray level based Hough transform for region extraction: An application to IRS images. Pattern Recognition Letters 19(2), 197–204 (1998)zbMATHGoogle Scholar
  176. 176.
    Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)Google Scholar
  177. 177.
    Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15, 201–221 (1994)Google Scholar
  178. 178.
    Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery and Soft Granular Computing. Chapman & Hall/CRC, Boca Raton (2004)zbMATHGoogle Scholar
  179. 179.
    Campbell, C., Cristianini, N., Smola, A.: Query learning with large margin classifiers. In: Proc. 17th Int. Conf. on Machine Learning, Stanford, CA, pp. 111–118. Morgan Kaufman, San Francisco (2000)Google Scholar
  180. 180.
    Mitra, P., Murthy, C.A., Pal, S.K.: Data condensation in large databases by incremental learning with support vector machines. In: Proc. Int. Conf. on Pattern Recognition (ICPR2000), Barcelona, Spain, pp. 712–715 (2000)Google Scholar
  181. 181.
    Tong, S., Koller, D.: Support vector machine active learning with application to text classification. J. Machine Learning Research 2, 45–66 (2001)Google Scholar
  182. 182.
    Schohn, G., Cohn, D.: Less is more: Active learning with support vector machines. In: Proc. 17th Int. Conf. on Machine Learning, Stanford, CA, pp. 839–846. Morgan Kaufman, San Francisco (2000)Google Scholar
  183. 183.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  184. 184.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 1–47 (1998)Google Scholar
  185. 185.
    Pal, S.K., Ghosh, A.: Fuzzy geometry in image analysis. Fuzzy Sets and Systems 48, 23–40 (1992)MathSciNetGoogle Scholar
  186. 186.
    Pal, S.K., Ghosh, A.: Image segmentation using fuzzy correlation. Information Sciences 62, 223–250 (1992)zbMATHGoogle Scholar
  187. 187.
    Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)MathSciNetGoogle Scholar
  188. 188.
    Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data. IEEE Press, New York (1992)Google Scholar
  189. 189.
    Pal, S.K., Majumder, D.D.: Fuzzy Mathematical Approach to Pattern Recognition. Halsted Press, New York (1986)zbMATHGoogle Scholar
  190. 190.
    Murthy, C.A., Pal, S.K.: Bounds for membership function: Correlation based approach. Information Sciences 65, 143–171 (1992)zbMATHMathSciNetGoogle Scholar
  191. 191.
    Murthy, C.A., Pal, S.K.: Histogram thresholding by minimizing gray level fuzziness. Information Sciences 60(1/2), 107–135 (1992)MathSciNetGoogle Scholar
  192. 192.
    Pal, S.K.: A note on the quantitative measure of image-enhancement through fuzziness. IEEE Trans. Pattern Analysis and Machine Intelligence 4, 204–208 (1982)zbMATHGoogle Scholar
  193. 193.
    Ghosh, A.: Use of fuzziness measures in layered networks for object extraction: A generalization. Fuzzy Sets and Systems 72, 331–348 (1995)Google Scholar
  194. 194.
    Pal, N.R., Pal, S.K.: Entropy: A new definition and its applications. IEEE Trans. Systems, Man and Cybernetics 21, 1260–1270 (1991)MathSciNetGoogle Scholar
  195. 195.
    Murthy, C.A., Pal, S.K., Majumder, D.D.: Correlation between two fuzzy membership functions. Fuzzy Sets and Systems 7, 23–38 (1985)Google Scholar
  196. 196.
    Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, New York (1973)zbMATHGoogle Scholar
  197. 197.
    Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)zbMATHGoogle Scholar
  198. 198.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic, Dordrecht (1992)Google Scholar
  199. 199.
    Komorouski, J., et al.: Rough sets: A tutorial. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A New Trend In Decision-Making, pp. 3–98. Springer, Singapore (1999)Google Scholar
  200. 200.
    Wojcik, Z.: Rough approximation of shapes in pattern recognition. Computer Vision, Graphics and Image Processing 40, 228–249 (1987)Google Scholar
  201. 201.
    Pal, S.K.: Fuzzy image processing and recognition: Uncertainties handling and applications. Int. J. Image and Graphics 1(2), 69–195 (2001)Google Scholar
  202. 202.
    Hough, P.V.C.: A method and means for recognizing complex patterns. Technical report (U.S. Patent 3069654) (1962)Google Scholar
  203. 203.
    Risse, T.: Hough transform for line recognition: Complexity of evidence accumulation and cluster detection. Computer Vision, Graphics and Image Processing 46, 327–345 (1989)Google Scholar
  204. 204.
    Duda, R.O., Hart, P.E.: Use of the Hough transform to detect lines and curves in pictures. Communications ACM 15, 11–15 (1972)Google Scholar
  205. 205.
    Lo, R., Tsai, W.: Gray-scale Hough transform for thick line detection in gray-scale images. Pattern Recognition 28, 647–661 (1995)Google Scholar
  206. 206.
    Basak, J., Pal, S.K.: Theoretical quantification of shape distortion in fuzzy Hough transform. Fuzzy Sets and Systems 154(2), 227–250 (2005)zbMATHMathSciNetGoogle Scholar
  207. 207.
    Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)zbMATHGoogle Scholar
  208. 208.
    Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)zbMATHGoogle Scholar
  209. 209.
    Beaubouef, T., Petry, F.E., Arora, G.: Information measure for rough and fuzzy sets and application to uncertainty in relational databases. In: Pal, S.K., Skowron, A. (eds.) Rough Fuzzy Hybridization: A new trend in decision-making, pp. 200–214. Springer, Singapore (1999)Google Scholar
  210. 210.
    Duntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998)MathSciNetGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • B. Uma Shankar
    • 1
  1. 1.Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108India

Personalised recommendations