Fusion of ETM+ Multispectral and Panchromatic Texture for Remote Sensing Classification

Authors: Mahesh Pal

Abstract:

This paper proposes to use ETM+ multispectral data and panchromatic band as well as texture features derived from the panchromatic band for land cover classification. Four texture features including one 'internal texture' and three GLCM based textures namely correlation, entropy, and inverse different moment were used in combination with ETM+ multispectral data. Two data sets involving combination of multispectral, panchromatic band and its texture were used and results were compared with those obtained by using multispectral data alone. A decision tree classifier with and without boosting were used to classify different datasets. Results from this study suggest that the dataset consisting of panchromatic band, four of its texture features and multispectral data was able to increase the classification accuracy by about 2%. In comparison, a boosted decision tree was able to increase the classification accuracy by about 3% with the same dataset.

Keywords: Internal texture; GLCM; decision tree; boosting; classification accuracy.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076902

References:

[1] Lu, D and Weng, Q, Urban Classification Using Full Spectral Information of Landsat ETM+ Imagery in Marion County, Indiana, Photogrammetric Engineering & Remote Sensing, 71(11), 2005, 1275- 1284.
[2] L.,Breiman, J.H. Friedman, R.A.,Olshen and C.J.,Stone, Classification and Regression Trees, Wadsworth, Monterey, CA, 1984.
[3] S. K.Murthy, S. Kasif and S. Salzberg, A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 2, 1994, 1-32.
[4] I. Kononenko and J. S. Hong, Attribute selection for modelling. Future Generation Computer Systems, 13, 1997, 181-195.
[5] J. Mingers, An empirical comparison of selection measures for decision tree induction. Machine Learning, 3, 1989, 319-342.
[6] J. R. Quinlan, C4.5: Programs for Machine Learning. San Mateo: Morgan Kaufmann, San Francisco, 1993.
[7] Y.Freund and R. Schapire, Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International conference, 148-156, 1996.
[8] M. A. Friedl, C. E. Brodley, and A. H. Strahler, Maximizing land cover classification accuracies produced by decision tree at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing. 37, 1999, 969-977.
[9] M. Pal and P. M. Mather, An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment. 86, 2003, 554-565.
[10] G. J. Briem, J. A. Benediktsson, and J. R. Sveinsson, Multiple Classifiers Applied to Multisource Remote Sensing Data IEEE Transactions on Geoscience and Remote Sensing, 40, 2002, 2291-2299.
[11] R.M. Harlick, K. Shanmugam and I. Dinstein, Texture features for image classification. IEEE Transactions on System, Man, and Cybernetics, 3(6), 1973, 610-621.