TEXTNN-A MATLAB program for textural classification using neural networks

Computers and Geosciences

Volumn 35, Issue 10, 2009, Pages 2084-2094

  Leite, Emilson Pereira ^a   de Souza Filho, Carlos Roberto ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Feed forward neural networks; RADAR images; Semivariograms; Supervised classification; Textural images

Indexed keywords

BEST MODEL; CROSS VALIDATION; END USERS; FEATURE VECTORS; FEEDFORWARD BACKPROPAGATION; FREQUENCY DOMAINS; GEOLOGIC MAPPING; GEOSCIENCES; KAPPA COEFFICIENT; MATLAB CODE; MATLAB PROGRAM; MEAN SQUARED ERROR; MOVING WINDOW; NEURAL NETWORK MODEL; OMNI-DIRECTIONAL; PRE-DEFINED CLASS; PREDICTIVE POWER; RADAR IMAGES; RADARSAT; SAR IMAGERY; SEMIVARIANCES; SEMIVARIOGRAMS; SPATIAL DOMAINS; SPLIT-SAMPLE; STANDARD DEVIATION; SUPERVISED CLASSIFICATION; SYNTHETIC IMAGES; TEST SETS; TEXTURAL CLASSIFICATION; TEXTURAL IMAGES; TEXTURAL INFORMATION; TRAINING PHASE; TRAINING SETS; VARIOGRAMS;

IMAGE CLASSIFICATION; IMAGING SYSTEMS; MATLAB; MEAN SQUARE ERROR; NEURAL NETWORKS; PROGRAM INTERPRETERS; STATISTICAL TESTS; SYNTHETIC APERTURE RADAR; SYNTHETIC APERTURES; THREE TERM CONTROL SYSTEMS;

PIXELS;

ARTIFICIAL NEURAL NETWORK; BACK PROPAGATION; IMAGE CLASSIFICATION; RADAR IMAGERY; SOFTWARE; VARIOGRAM;

EID: 70249114053     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2008.10.009     Document Type: Article

References (34)

1. Avriel M. Nonlinear Programming: Analysis and Methods. second ed (2003), Dover Publishing, Dover, UK p. 548
2. Berry M.J.A., and Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support. second ed (2004), Wiley, New York, USA p. 672
3. Bishop C. Neural Networks for Pattern Recognition. first ed (1995), Oxford University Press, Oxford, UK p. 504
4. Boger, Z., Guterman, H., 1997. Knowledge extraction from artificial neural network models. In: Systems, Man, and Cybernetics. Proceedings of the IEEE International Conference on Computational Cybernetics and Simulation, Orlando, FL, USA, Vol. 4. pp. 3030-3035.
5. Carr J.R. Spectral and textural classification of single and multiple band digital images. Computers & Geosciences 22 8 (1996) 849-865
6. Carr J.R., and Miranda F.P. The semivariogram in comparison to the co-occurrence matrix for classification of image texture. IEEE Transactions on Geoscience and Remote Sensing 36 (1998) 1945-1952
7. Chica-Olmo M., and Arbarca-Hérnandez F. Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences 26 4 (2000) 373-383
8. Dekker R.J. Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands. IEEE Transactions on Geoscience and Remote Sensing 41 9 (2003) 1950-1958
9. Demuth, H., Beale, M., Hagan, M., 2008. Neural Network Toolbox™ 6. User's Guide. The MathWorks™, Natick, MA, 907p.
10. Foody G.M. On the compensation for change agreement in image classification accuracy assessment. Photogrammetric Engineering and Remote Sensing 6 10 (1992) 1459-1460
11. Gao D. Texture model regression for effective feature discrimination: application to seismic facies visualization and interpretation. Geophysics 69 (2004) 958-967
12. Gao D. Application of seismic texture model regression to seismic facies characterization and interpretation. The Leading Edge 27 (2008) 394-397
13. Hagan M.T., Demuth H.B., and Beale M.H. Neural Network Design. first ed (1995), PWS Publishing, Boston, MA, USA p. 736
14. Haykin S. Neural Networks: A Comprehensive Foundation. third ed (2008), Prentice Hall, Upper Saddle River, NJ, USA p. 906
15. Hjorth J.S.U. Computer Intensive Statistical Methods Validation, Model Selection, and Bootstrap. first ed (1993), Chapman & Hall, London, UK p. 263
16. Kohavi R., and Provost F. Glossary of terms: editorial for the special issue on applications of machine learning and the knowledge discovery process. Machine Learning 30 (1998) 217-274
17. Kurosu T., Yokoyama S., Fujita M., and Chiba K. Land-use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L-band SAR images. International Journal of Remote Sensing 22 4 (2001) 595-613
18. Li P., Li Z., and Moon W.M. Lithological discrimination of Altun area in northwest China using Landsat TM data and geostatistical textural information. Geosciences Journal 5 4 (2001) 293-300
19. Mandic D.P., and Chambers J.A. Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability. first ed (2001), Wiley, Chichester, UK p. 306
20. Marcotte D. Fast variogram computation with FFT. Computers & Geosciences 22 10 (1996) 1175-1186
21. Marmo R., Amodio S., Tagliaferri R., Ferreri V., and Longo G. Textural identification of carbonate rocks by image processing and neural network: methodology proposal and examples. Computers & Geosciences 31 5 (2005) 649-659
22. Miranda F.P., Fonseca L.E.N., and Carr J.R. Semivariogram textural classification of JERS-1 (Fuyo-1) SAR data obtained over a flooded area of the Amazon rainforest. International Journal of Remote Sensing 19 (1998) 549-556
23. Miranda F.P., Marmol A.M.Q., Pedroso E.C., Beisl C.H., Welgan P., and Morales L.M. Analysis of RADARSAT-1 data for offshore monitoring activities in the Cantarell Complex, Gulf of Mexico, using the unsupervised semivariogram textural classifier (USTC). Canadian Journal of Remote Sensing 30 3 (2004) 424-436
24. Moysey S., Knight R.J., and Jol H.M. Texture-based classification of ground-penetrating radar images. Geophysics 71 6 (2006) K111-K118
25. Novak L.M., Owirka G.J., and Netishen C.M. Performance of a high-resolution polarimetric SAR automatic target recognition system. The Lincoln Laboratory Journal 6 1 (1993) 11-23
26. Nguyen, D., Widrow. B., 1990. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. In: Proceedings of the International Joint Conference on Neural Networks 3, Washington, DC, USA, pp. 21-26.
27. Plutowski M., Sakata S., and White H. Cross-validation estimates IMSE. In: Cowan J.D., Tesauro G., and Alspector J. (Eds). Advances in Neural Information Processing Systems 6 (1994), Morgan Kaufman, San Mateo, CA, USA 391-398
28. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rumelhart D.E., and Mclelland J.L. (Eds). Parallel Distributed Processing 1 (1986), MIT Press, Cambridge, MA, USA 318-362
29. Schobbenhaus, C., Gonçalves, J.H., Santos, J.O.S., Abram, M.B., Leão Neto, R., Matos, G.M.M., Vidotti, R.M., Ramos, M.A.B., Jesus, J.D.A., 2004. Geological chart of Brazil 1:1 Million GIS. In: Proceedings of the 32nd International Geological Congress, 2004, Florence, Italy.
30. Schröder R., Puls J., Hanjnsek I., Jochim F., Neff T., Kono J., Paradella W.R., Silva M.M.Q., Valeriano D.M., and Maycira P.F.C. MAPSAR: a small L-band SAR mission for land observation. Acta Astronautica 56 (2005) 35-43
31. Snyman J. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. first ed (2005), Springer Publishing, New York, USA p. 257
32. Swingler K. Applying Neural Networks: A Practical Guide. first ed (1996), Morgan Kaufmann Publishers, Inc., San Francisco, CA, USA p. 303
33. Tercier P., Knight R.J., and Jol H.M. A comparison of the correlation structure in GPR images of deltaic and barrier-spit depositional environments. Geophysics 65 (2000) 1142-1153
34. Weiss S.M., and Kulikowski C.A. Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. first ed (1991), Morgan Kaufmann Publishers, San Francisco, CA, USA p. 223