Skip to main content
SpringerLink
Log in

Novel Classification and Segmentation Techniques with Application to Remotely Sensed Images

  • Chapter
Transactions on Rough Sets VII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4400))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Osteaux, M., Meirleir, K.D., Shahabpour, M. (eds.): Magnetic Resonance Imaging and Spectroscopy in sports medicine. Springer, Berlin (1991)

    Google Scholar 

  2. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Pearson Education, Inc., Singapore (2002)

    Google Scholar 

  3. Hall, E.L.: Computer Image Processing and Recognition. Academic Press, New York (1979)

    MATH  Google Scholar 

  4. Marr, D.: Vision. Freeman, San Fransicsco (1982)

    Google Scholar 

  5. Rosenfeld, A., Kak, A.C.: Digital Picture Processing, vol. I & II. Academic Press, New York (1982)

    Google Scholar 

  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. 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. 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. 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. 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. Chow, C., Kaneko, T.: Automatic boundary detection of the left ventricle from cineangiograms. Computers and Biomedical Research 5, 388–341 (1972)

    Google Scholar 

  12. Nakagawa, Y., Rosenfeld, A.: Some experiments on variable thresholding. Pattern Recognition 11, 191–204 (1979)

    Google Scholar 

  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. 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. 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. Lloyd, D.E.: Automatic target classification using moment invariants of image shapes. Report RAE IDN AW126, Farnborough, UK (1985)

    Google Scholar 

  17. Otsu, N.: A threshold selection method from grey-level histograms. IEEE Trans. Systems, Man and Cybernetics 9, 62–66 (1979)

    Google Scholar 

  18. Kittler, J., Illingworth, J.: Minimum error thresholding. Pattern Recognition 19(1), 41–47 (1986)

    Google Scholar 

  19. Pal, N.R., Bhandari, D.: On object-background classification. Int. J. Systems Science 23, 1903–1920 (1992)

    MATH  MathSciNet  Google Scholar 

  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. 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. 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. 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. Weszka, J.S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Trans. Systems, Man and Cybernetics 8, 622–629 (1978)

    Google Scholar 

  25. Deravi, F., Pal, S.K.: Gray level thresholding using second order statistics. Pattern Recognition Letters 1, 417–422 (1983)

    Google Scholar 

  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. 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. Pal, N.R., Pal, S.K.: Entropic thresholding. Signal Processing 106, 97–108 (1989)

    Google Scholar 

  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. Peleg, S.: A new probabilistic relaxation scheme. IEEE Trans. Pattern Analysis and Machine Intelligence 2, 362–369 (1980)

    MATH  Google Scholar 

  31. Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Trans. Systems, Man and Cybernetics 6, 420–433 (1976)

    MATH  MathSciNet  Google Scholar 

  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. 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)

    MATH  Google Scholar 

  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)

    MATH  Google Scholar 

  35. Kohonen, T.: Self-organization and Associative Memory. Springer, New York (1989)

    Google Scholar 

  36. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, New York (1989)

    MATH  Google Scholar 

  37. Babaguchi, N., et al.: Connectionist model binarization. In: Proc. 10th ICPR, pp. 51–56 (1990)

    Google Scholar 

  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. 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. Ghosh, A., Pal, N.R., Pal, S.K.: Image segmentation using neural networks. Biological Cybernetics 66(2), 151–158 (1991)

    MATH  Google Scholar 

  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. 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. 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. 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. 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. 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. 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. Jiang, Y., Zhou, Z.: SOM ensemble-based image segmentation. Neural Processing Letters 20(3), 171–178 (2004)

    Google Scholar 

  49. Gonzalez, R.C., Wintz, P.: Digital Image Processing. Addison-Wesley, Reading (1987)

    Google Scholar 

  50. Davis, L.S.: A survey of edge detection techniques. Computer Graphics and Image Processing 4(3), 248–270 (1975)

    Google Scholar 

  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. Haddon, J.F.: Generalized threshold selection for edge detection. Pattern Recognition 21, 195–203 (1988)

    Google Scholar 

  53. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    MATH  MathSciNet  Google Scholar 

  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. Pal, S.K., King, R.A.: Image enhancement using fuzzy sets. Electronic Letters 16, 376–378 (1980)

    Google Scholar 

  56. Pal, S.K., Rosenfeld, A.: Image enhancement and thresholding by optimization of fuzzy compactness. Pattern Recognition Letters 7(2), 77–86 (1988)

    MATH  Google Scholar 

  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)

    MATH  Google Scholar 

  58. Pal, S.K., Dasgupta, A.: Spectral fuzzy sets and soft thresholding. Information Sciences 65, 65–97 (1992)

    MATH  MathSciNet  Google Scholar 

  59. Xie, W.X., Bedrosian, S.D.: Experimentally driven fuzzy membership function for gray level images. J. Franklin Institute 325, 154–164 (1988)

    MathSciNet  Google Scholar 

  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. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)

    MATH  Google Scholar 

  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. Huntsberger, T.L., Jacobs, C.L., Cannon, R.L.: Iterative fuzzy image segmentation. Pattern Recognition 18, 131–138 (1985)

    Google Scholar 

  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. 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)

    MATH  Google Scholar 

  66. Keller, J.M., Carpenter, C.L.: Image segmentation in presence of uncertainty. Int. J. Intelligent Systems 5, 193–208 (1990)

    MATH  Google Scholar 

  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)

    MathSciNet  Google Scholar 

  68. Meyer, F.: Topographic distance and watershed lines. Signal Processing 38(1), 113–125 (1994)

    MATH  Google Scholar 

  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:10.1109/34.910886

    Google Scholar 

  70. Bhanu, B., Fonder, S.: Functional template-based SAR image segmentation. Pattern Recognition 37(1), 61–77 (2004)

    Google Scholar 

  71. Bhanu, B., Lee, S.: Genetic Learning for Adaptive Image Segmentation. Kluwer Academic Publishers, Norwell (1994)

    MATH  Google Scholar 

  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. Heiler, M., Schnörr, C.: Natural image statistics for natural image segmentation. Int. J. Computer Vision 63(1), 5–19 (2005)

    Google Scholar 

  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. Kervrann, C., Trubuil, A.: Optimal level curves and global minimizers of cost functionals in image segmentation. J. Mathematical Imaging 17(2), 153–174 (2002)

    MATH  MathSciNet  Google Scholar 

  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. Pal, S.K., Ghosh, A., Kundu, M.K.: Soft Computing for Image Processing. Physica, Heidelberg (2000)

    MATH  Google Scholar 

  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)

    MATH  MathSciNet  Google Scholar 

  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. Ohlander, R.B.: Analysis of natural scenes. PhD thesis, Department of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania (1975)

    Google Scholar 

  81. Overheim, R.D., Wagner, D.L.: Light and Color. Wiley, New York (1982)

    Google Scholar 

  82. Cheng, H.D., et al.: Color image segmentation: Advances and prospects. Pattern Recognition 34(12), 2259–2281 (2001)

    MATH  Google Scholar 

  83. Naik, S.K., Murthy, C.A.: Standardization of edge magnitude in color images. IEEE Trans. Image Processing, Communicated (2005)

    Google Scholar 

  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. 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. 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. Pal, N.R., Bhandari, D.: Object background classification: Some new techniques. Signal Processing 33(2), 139–158 (1993)

    MATH  Google Scholar 

  88. Brink, A.B.: Gray level thresholding of images using a correlation criterion. Pattern Recognition Letters 9, 335–341 (1989)

    MATH  Google Scholar 

  89. Pal, N.R., Pal, S.K.: A review on image segmentation techniques. Pattern Recognition 26(6), 1277–1294 (1993)

    Google Scholar 

  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. Egmont-Petersen, M., de Ridder, D., Handels, H.: Image processing with neural networks - a review. Pattern Recognition 35(10), 2279–2301 (2002)

    MATH  Google Scholar 

  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. Fu, K.S., Mui, J.K.: A survey on image segmentation. Pattern Recognition 13, 3–16 (1981)

    MathSciNet  Google Scholar 

  94. Haralick, R.M., Shapiro, L.G.: Survey, image segmentation techniques. Computer Vision, Graphics and Image Processing 29, 100–132 (1985)

    Google Scholar 

  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. Sahoo, P.K., et al.: A survey of thresholding techniques. Computer Vision, Graphics and Image Processing 41(2), 233–260 (1988)

    Google Scholar 

  97. Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognition 29(8), 1335–1346 (1996)

    Google Scholar 

  98. Mather, P.M.: Computer Processing of Remotely-Sensed Images: An Introduction. John Wiley & Sons, Chichester (1999)

    Google Scholar 

  99. Richards, J.A., Jia, X.: Remote Sensing Digital Image Analysis: An Introduction, 3rd edn. Springer, Heidelberg (1999)

    Google Scholar 

  100. Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 2nd edn. Academic Press, San Diego (1997)

    Google Scholar 

  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. Swain, P.H., Davis, S.M.: Remote Sensing: The Quantitative Approach. McGraw Hill Inc., New York (1978)

    Google Scholar 

  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. 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. Lee, C., Landgrebe, D.A.: Fast multistage likelihood classification. IEEE Trans. Geoscience and Remote Sensing 29(4), 509–517 (1991)

    Google Scholar 

  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. 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. 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. 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. 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.2003.11.011

    Google Scholar 

  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. 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. 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. 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.2003.09.006

    MathSciNet  Google Scholar 

  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. 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. 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. 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. Swain, P.H., Hauska, H.: The decision tree classifier: Design and potential. IEEE Trans. Geoscience Electronics 15, 142–147 (1977)

    Google Scholar 

  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. 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. 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. 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. Laprade, R.H.: Split-and-merge segmentation of aerial photographs. Computer Vision, Graphics and Image Processing 44(1), 77–86 (1988)

    Google Scholar 

  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. 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. 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. 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. 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. 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. Wang, F.: Fuzzy supervised classification of remote sensing images. IEEE Trans. Geoscience and Remote Sensing 28(2), 194–201 (1990)

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.2003.11.024

    Google Scholar 

  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. Villmann, T., Merényi, E., Hammer, B.: Neural maps in remote sensing image analysis. Neural Networks 16(3-4), 389–403 (2003)

    Google Scholar 

  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. 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. Ton, J.: A Knowledge Based Approach for Landsat Image Interpretation. PhD thesis, Michigan State University, Michigan, USA (1988)

    Google Scholar 

  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. 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. Bandyopadhyay, S., Maulik, U.: Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recognition 35(2), 1197–1208 (2002)

    MATH  Google Scholar 

  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:10.1109/34.85677

    Google Scholar 

  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. 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. Varshney, P.K., Arora, M.K.: Advanced Image Processing Techniques for Remote Sensed Hyperspectral Data. Springer, Heidelberg (2004)

    Google Scholar 

  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)

    MATH  Google Scholar 

  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. 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. 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. 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. Zlotnick, A., Carnine Jr., P.D.: Finding road seeds in aerial images. CVGIP: Image Understanding 57(2), 243–260 (1993)

    Google Scholar 

  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. 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:10.1109/34.476006

    Google Scholar 

  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. 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. 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. 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. 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. 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. 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. 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. 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.2005.05.007

    Google Scholar 

  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)

    MATH  Google Scholar 

  176. Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)

    Google Scholar 

  177. Cohn, D., Atlas, L., Ladner, R.: Improving generalization with active learning. Machine Learning 15, 201–221 (1994)

    Google Scholar 

  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)

    MATH  Google Scholar 

  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. 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. 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. 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. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  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. Pal, S.K., Ghosh, A.: Fuzzy geometry in image analysis. Fuzzy Sets and Systems 48, 23–40 (1992)

    MathSciNet  Google Scholar 

  186. Pal, S.K., Ghosh, A.: Image segmentation using fuzzy correlation. Information Sciences 62, 223–250 (1992)

    MATH  Google Scholar 

  187. Shannon, C.E.: A mathematical theory of communication. Bell System Technical Journal 27, 379–423 (1948)

    MathSciNet  Google Scholar 

  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. Pal, S.K., Majumder, D.D.: Fuzzy Mathematical Approach to Pattern Recognition. Halsted Press, New York (1986)

    MATH  Google Scholar 

  190. Murthy, C.A., Pal, S.K.: Bounds for membership function: Correlation based approach. Information Sciences 65, 143–171 (1992)

    MATH  MathSciNet  Google Scholar 

  191. Murthy, C.A., Pal, S.K.: Histogram thresholding by minimizing gray level fuzziness. Information Sciences 60(1/2), 107–135 (1992)

    MathSciNet  Google Scholar 

  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)

    MATH  Google Scholar 

  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. Pal, N.R., Pal, S.K.: Entropy: A new definition and its applications. IEEE Trans. Systems, Man and Cybernetics 21, 1260–1270 (1991)

    MathSciNet  Google Scholar 

  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. Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, New York (1973)

    MATH  Google Scholar 

  197. Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)

    MATH  Google Scholar 

  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. 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. Wojcik, Z.: Rough approximation of shapes in pattern recognition. Computer Vision, Graphics and Image Processing 40, 228–249 (1987)

    Google Scholar 

  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. Hough, P.V.C.: A method and means for recognizing complex patterns. Technical report (U.S. Patent 3069654) (1962)

    Google Scholar 

  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. 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. 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. Basak, J., Pal, S.K.: Theoretical quantification of shape distortion in fuzzy Hough transform. Fuzzy Sets and Systems 154(2), 227–250 (2005)

    MATH  MathSciNet  Google Scholar 

  207. Ballard, D.H.: Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition 13, 111–122 (1981)

    MATH  Google Scholar 

  208. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  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. Duntsch, I., Gediga, G.: Uncertainty measures of rough set prediction. Artificial Intelligence 106, 109–137 (1998)

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108, India

    B. Uma Shankar

Authors
  1. B. Uma Shankar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

James F. Peters Andrzej Skowron Victor W. Marek Ewa Orłowska Roman Słowiński Wojciech Ziarko

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this chapter

Cite this chapter

Shankar, B.U. (2007). Novel Classification and Segmentation Techniques with Application to Remotely Sensed Images. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-71663-1_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71662-4

  • Online ISBN: 978-3-540-71663-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation