The Impact of Feature Selection on Urban Land Cover Classification

Turgut Dogan, Alper Kursat Uysal
  • Alper Kursat Uysal
    Anadolu University, Turkey


Many of the studies in the literature about land cover classification are focused on the feature extraction and classification rather than feature selection. In this paper, the impact of feature selection on urban land cover classification is extensively analyzed. Three types of features namely spectral, texture, and size/shape features are used for this analysis. This analysis is carried out using three variations of a filter based feature selection method and three widely-known classification algorithms. The feature selection method used for the comparison is a multivariate filter method namely correlation-based feature subset selection where a feature subset evaluator and a search method are integrated. Best first search, genetic search, and greedy stepwise search are three different search methods used for this integration. The classification algorithms employed are Bayesian network, random forest, and support vector machine. The experimental results explicitly indicate that feature selection improves classification accuracy in all cases.  Besides, according to the experimental results, random forest classifier is the most successful one among these three classifiers while both feature selection is applied and not applied. Largest improvement in the classification performance is obtained when greedy stepwise search based feature selection method and support vector machine classifier is applied together. Also, the contribution of spectral features to the performance of classification is more than size/shape and texture features.


Classification, Feature selection, Land cover, Remote Sensing.

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Submitted: 2017-11-01 15:44:28
Published: 2018-03-29 15:53:50
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X. Tong, H. Xie, and Q. Weng, "Urban land cover classification with airborne hyperspectral data: What features to use?," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 10, pp. 3998-4009, 2014.

S. Kandrika and P. S. Roy, "Land use land cover classification of Orissa using multi-temporal IRS-P6 awifs data: A decision tree approach," International Journal of Applied Earth Observation and Geoinformation, vol. 10, no. 2, pp. 186-193, 2008.

L. Ma, M. Li, X. Ma, L. Cheng, P. Du, and Y. Liu, "A review of supervised object-based land-cover image classification," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 130, pp. 277-293, 2017.

P. Zope, T. Eldho, and V. Jothiprakash, "Impacts of land use–land cover change and urbanization on flooding: a case study of Oshiwara River Basin in Mumbai, India," Catena, vol. 145, pp. 142-154, 2016.

W. Y. Yan, A. Shaker, and N. El-Ashmawy, "Urban land cover classification using airborne LiDAR data: A review," Remote Sensing of Environment, vol. 158, pp. 295-310, 2015.

Q. Yu, P. Gong, N. Clinton, G. Biging, M. Kelly, and D. Schirokauer, "Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery," Photogrammetric Engineering & Remote Sensing, vol. 72, no. 7, pp. 799-811, 2006.

T. Blaschke, "Object based image analysis for remote sensing," ISPRS journal of photogrammetry and remote sensing, vol. 65, no. 1, pp. 2-16, 2010.

R. C. Weih and N. D. Riggan, "Object-based classification vs. pixel-based classification: Comparative importance of multi-resolution imagery," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 38, no. 4, p. C7, 2010.

T. Blaschke, C. Burnett, and A. Pekkarinen, "Image segmentation methods for object-based analysis and classification," in Remote sensing image analysis: Including the spatial domain: Springer, 2004, pp. 211-236.

S. W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, "Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery," Remote sensing of environment, vol. 115, no. 5, pp. 1145-1161, 2011.

N. Thomas, C. Hendrix, and R. G. Congalton, "A comparison of urban mapping methods using high-resolution digital imagery," Photogrammetric Engineering & Remote Sensing, vol. 69, no. 9, pp. 963-972, 2003.

D. C. Duro, S. E. Franklin, and M. G. Dubé, "A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery," Remote Sensing of Environment, vol. 118, pp. 259-272, 2012.

J. Aguirre-Gutiérrez, A. C. Seijmonsbergen, and J. F. Duivenvoorden, "Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in Mexico," Applied Geography, vol. 34, pp. 29-37, 2012.

T. Rittl, M. Cooper, R. Heck, and M. Ballester, "Object-based method outperforms per-pixel method for land cover classification in a protected area of the Brazilian Atlantic rainforest region," Pedosphere, vol. 23, no. 3, pp. 290-297, 2013.

B. Johnson and Z. Xie, "Classifying a high resolution image of an urban area using super-object information," ISPRS journal of photogrammetry and remote sensing, vol. 83, pp. 40-49, 2013.

M. N. Jebur, H. Z. Mohd Shafri, B. Pradhan, and M. S. Tehrany, "Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery," Geocarto International, vol. 29, no. 7, pp. 792-806, 2014.

M. Bochow, H. Taubenböck, K. Segl, and H. Kaufmann, "An automated and adaptable approach for characterizing and partitioning cities into urban structure types," in Geoscience and Remote Sensing Symposium (IGARSS), 2010 IEEE International, 2010, pp. 1796-1799: IEEE.

M. Pal and G. M. Foody, "Feature selection for classification of hyperspectral data by SVM," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 5, pp. 2297-2307, 2010.

Y. Maghsoudi, M. J. Valadan Zoej, and M. Collins, "Using class-based feature selection for the classification of hyperspectral data," International journal of remote sensing, vol. 32, no. 15, pp. 4311-4326, 2011.

G. Myburgh and A. Van Niekerk, "Effect of feature dimensionality on object-based land cover classification: A comparison of three classifiers," South African Journal of Geomatics, vol. 2, no. 1, pp. 13-27, 2013.

B. Banerjee, A. Bhattacharya, and K. M. Buddhiraju, "A generic land-cover classification framework for polarimetric SAR images using the optimum Touzi decomposition parameter subset—An insight on mutual information-based feature selection techniques," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1167-1176, 2014.

M. A. Hall, "Correlation-based feature selection for machine learning," 1999.

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: an update," ACM SIGKDD explorations newsletter, vol. 11, no. 1, pp. 10-18, 2009.

A. K. Pandey, P. Pandey, K. Jaiswal, and A. K. Sen, "DataMining Clustering Techniques in the Prediction of Heart Disease using Attribute Selection Method," heart disease, vol. 14, pp. 16-17, 2013.

M. D. Patil and D. S. S. Sane, "Effective Classification after Dimension Reduction: A Comparative Study," International Journal of Scientific and Research Publications, vol. 4, no. 7, p. 1, 2014.

A. K. Uysal and S. Gunal, "Text classification using genetic algorithm oriented latent semantic features," Expert Systems with Applications, vol. 41, no. 13, pp. 5938-5947, 2014.

D. E. Goldberg, "Genetic algorithms in search, optimization, and machine learning, 1989," Reading: Addison-Wesley, 1989.

R. O. Mujalli and J. De ONa, "A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks," Journal of safety research, vol. 42, no. 5, pp. 317-326, 2011.

S. Theodoridis and K. Koutroumbas, Pattern Recognition, Fourth Edition. Academic Press, 2008, p. 900.

C. Huang, L. Davis, and J. Townshend, "An assessment of support vector machines for land cover classification," International Journal of remote sensing, vol. 23, no. 4, pp. 725-749, 2002.

N. Friedman, D. Geiger, and M. Goldszmidt, "Bayesian network classifiers," Machine learning, vol. 29, no. 2-3, pp. 131-163, 1997.

M. Pal, "Random forest classifier for remote sensing classification," International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, 2005.

G. Mountrakis, J. Im, and C. Ogole, "Support vector machines in remote sensing: A review," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 66, no. 3, pp. 247-259, 2011.

T. Kavzoglu and I. Colkesen, "A kernel functions analysis for support vector machines for land cover classification," International Journal of Applied Earth Observation and Geoinformation, vol. 11, no. 5, pp. 352-359, 2009.

J. Cheng and R. Greiner, "Comparing Bayesian network classifiers," in Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, 1999, pp. 101-108: Morgan Kaufmann Publishers Inc.

P. Larrañaga, M. Poza, Y. Yurramendi, R. H. Murga, and C. M. H. Kuijpers, "Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters," IEEE transactions on pattern analysis and machine intelligence, vol. 18, no. 9, pp. 912-926, 1996.

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