References

Agarwal, R. and Srikant, R. 1994, Fast algorithms for mining association rules. In Proceedings of the 1994 International Conference VLDB, pp. 487-499, Santiago, Chile.

Atkinson, P. M., and Aplin, P., 2004, Spatial variation in land cover and choice of spatial resolution for remote sensing. International Journal of Remote Sensing, vol. 25, pp. 3687-3702.

Atkinson P. M., and Tatnall, A. R. L., 1997, Introduction: neural networks in remote sensing. International Journal of Remote Sensing, vol. 18(4), pp. 699-709.

Bateson, C. A., Asner, G. P.., and Wessman, C. A., 2000, Endmember Bundles: A New Approach to Incorporating Endmember Variability into Spectral Mixture Analysis. IEEE Transactions on Geoscience and Remote Sensing, vol. 38, no. 2, pp. 1083-1094.

Beekhuizen, J., and Clarke, K. C., 2010, Toward accountable land use mapping: Using geocomputation to improve classification accuracy and reveal uncertainty. International Journal of Applied Earth Observation and Geoinformation, vol. 12, pp. 127-137.

Bischof, H., Schneider, W., and Pinz, A. J., 1992, Multispectral classification of Landsat images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, vol. 30(3), pp. 482-490.

Bouman, C., and Shapiro, M., 1992, Multispectral Image Segmentation using a Multiscale Image Model. Proceedings of IEEE International Conference on Acoust., Speech and Signal Processing, pp. III-565-III-568, San Francisco, California, March 23-26, 1992.

Bouman, C. and Shapiro, M., 1994, A Multiscale Random Field Model for Bayesian Image Segmentation. IEEE Transaction on Image Processing, vol. 3(2), pp. 162-177.
URL: http://wgbis.ces.iisc.ernet.in/grass/grass70/manuals/html70_user/i.smap.html

Breiman, L., 1998, Arcing classifiers (discussion paper). Annals of Statistics, vol. 26, pp. 801-824.

Breiman, L., 2001, Random Forests. Machine Learning, vol. 40, pp. 5-32.

Breiman, L., and Cutler, A., 2005. “Random Forests”.
URL: http://www.stat.berkeley.edu/users/breiman/RandomForests/

Breiman, L., and Cutler, A., 2010, Breiman and Cutler’s random forests for classification and regression. Version 4.5-36, Repository – CRAN.
URL: http://ugrad.stat.ubc.ca/R/library/randomForest/html/00Index.html

Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A.., Classification and Regression Trees (3rd Ed.), CRC Press, Boca Raton, Fl., 1998, pp. 372.

Cetin, H., Warner, T. A., and Levandowski, D. W., 1993, Data classification, visualization, and enhancement using n-dimensional probability density functions (NPDF): AVIRIS, TIMS, TM, and geophysical applications. Photogrammetric Engineering & Remote Sensing, vol. 59, pp. 1755–1764.

Chang, C-C., and Lin, C.-J., 2001, LIBSVM: a library for support vector machines.

Chang, C-I., 2005, Orthogonal Subspace Projection (OSP) Revisited: A Comprehensive Study and Analysis. IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 3, pp. 502–518.

Chang, D., and Islam, S., 2000, Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment, vol. 74(3), pp. 534-544.

Chauhan, H. B., and Dwivedi, R. M., 2008, Inter sensor comparison between RESOURCESAT LISS III, LISS IV and AWiFS with reference to coastal landuse/landcover studies. International Journal of Applied Earth Observation and Geoinformation, vol. 10(2), pp. 181-185.

Chen, D., Stow, D.A., and Gong, P., 2004, Examining the effect of spatial resolution and texture window size on classification accuracy: an urban environment case. International Journal of Remote Sensing, vol. 25, pp. 2177-2192.

Cortes, C. and Vapnik, V. 1995, Support-vector network. Machine Learning, vol. 20, pp 273-297.

Dasarathy, B. V. (Editor), Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos, California, 1990.

Dempster, A., Laird, N., and Rubin, D., 1977, Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Society Series B of Statistics,vol. 39(1), pp. 1-38.

Dixon, B. and Candade, N., 2008, Multispectral land use classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, vol. 29, pp. 1185-1206.

Duda, R. O., Hart, P. E., and Stork, D, G., Pattern classification, New York, A Wiley-Interscience Publication, Second Edition, 2000, ISBN 9814-12-602-0.

Dungan, J. L., Towards a comprehensive view of uncertainty in remote sensing analysis. In Foody, G. M., and Atkinson, P. M. (Eds), Uncertainty in Remote Sensing and GIS, pp. 23-35, Chichester: John Wiley & Sons, 2002.

Eva, H., Carboni, S., Achard, F., Stach, N., Durieux, L., and Faure, J-F., 2010, Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65(2), pp. 191-197.

Fayyad, U. M., Piatetsky-Shapiro, G.., Smyth, P., and Uthurusamy, R., editors. Advances in Knowledge in discovery and Data Mining. AAAI / MIT Press, Menlo Park, CA, 1996.

Foody, G. M., and Doan, H. T. X., 2007, Variability in Soft Classification Prediction and its Implications for Sub-pixel Scale Change Detection and Super Resolution Mapping. Photogrammetric Engineering & Remote Sensing, vol. 73, no. 8, pp. 923–933.

Freund, Y., and Schapire, R. E., 1996, Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning. pp. 148-156.

Friedl, M. A., Mcgwire, K. C., and Mciver, D. K., 2001, An overview of uncertainty in optical remotely sensed data for ecological applications. In Hunsaker, C. T., Goodchild, M. F., Friedl, M. A., and Case, T. J. (Eds), Spatial Uncertainty in Ecology: Implications for remote sensing and GIS applications, New York: Springer-Verlag, pp. 258-283.

Fukushima, K., 1988, A Neural Network for Visual Pattern Recognition. IEEE Computer, vol. 21(3), pp. 65-74.

Gislason, P. O., Benediktsson, J. A., and Sveinsson, J. R., 2006, Random Forests for land cover classification, Pattern Recognition Letters, vol. 27, pp. 294-300.

Gori, M., and Scarselli, F., 1998, Are Multilayer Perceptrons Adequate for Pattern Recognition and Verification?. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20(11), pp. 1121-1132.

Ham, J., Yangchi, C., Crawford, M. M., and Ghosh, J., 2005, Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, vol. 43(3), pp. 492-501.

Han, J., and Fu, Y. Exploration of the power of Attribute oriented Induction in Data Mining. Advances in Knowledge in discovery and Data Mining. AAAI / MIT Press, Menlo Park, CA, 1996.

Han, J., and Kamber, M., Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, CA, USA, 2003.

Hardin, P. J., 1994, Parametric and Nearest-neighbor methods for hybrid classification: A comparison of pixel assignment accuracy. Photogrammetric Engineering & Remote Sensing, vol. 60, pp. 1439-1448.

Haykin, S., Neural Networks: A Comprehensive Foundation. Prentice-Hall International, Englewood Cli6s, NJ, 1999.

He, L-M., Kong, F-S., and Shen, Z-Q., 2005, Multiclass SVM Based Land Classification with Multisource Data. In Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August, 2005.

Heermann, P. D., and Khazenie, N., 1992, Classification of multispectral remote sensing data using back-propagation neural network. IEEE Transactions on Geoscience and Remote Sensing, vol. 30(1), pp. 81-88.

Hsu, C.-W., Chang, C.-C., and Lin, C.-J., 2007, A practical guide to support vector classification. National Taiwan University.
URL: http://ntu.csie.org/~cjlin/papers/guide/guide.pdf

Jain, A. K. and Dubes, R. C., Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice Hall, 1988.

Janssen, L. L. F., and Middelkoop, H., Knowledge-based crop classification of a Landsat Thematic Mapper imag. Photogrammetric Engineering & Remote Sensing, vol. 13, pp. 2827–2837, 1992.

Joelsson, S. R., Benediktsson, J. A., and Sveinsson, J. R., 2005, Random forest classifiers for hyperspectral data. Geoscience and Remote Sensing Symposium, 2005. IGARSS '05. Proceedings. 2005 IEEE International, vol.1, pp. 4, 25-29 July 2005.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1526129&isnumber=32595

Johnson, R. A., and Wichern, D. W., Applied Multivariate Statistical Analysis, Pearson Education, Second Indian Reprint, New Delhi, India, 2005, ISBN 81-7808-686-7, pp. 591-592 and 610-611.
Kavzoglu, T., and Colkesen, I., 2009, A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, vol. 11(5), pp. 352-359.

Kavzoglu, T., and Mather, P. M., 1999, Pruning artificial neural networks: an example using land cover classification of multi-sensor images. International Journal of Remote Sensing, vol. 20(14), pp. 2787-2803.

Kavzoglu, T., and Mather, P. M., 2003, The use of back propagation artificial neural networks in land cover classification. International Journal of Remote Sensing, vol. 24(23), pp. 4907-4938.

Kumar, U., Kumar Raja, S., Mukhopadhyay, C., and Ramachandra T. V., 2011, Hybrid Bayesian Classifier for Improved Classification Accuracy. IEEE Geoscience and Remote Sensing Letters, vol. 8(3), pp. 473-476.

Kumar, U., Kerle, N., and Ramachandra, T. V., Constrained linear spectral unmixing technique for regional land cover mapping using MODIS data. In: Innovations and advanced techniques in systems, computing sciences and software engineering/ed by Khaled Elleithy, Berlin: Springer, pp. 87-95, 2008.

Kwan, H. K., and Cai, Y., 1994, A Fuzzy Neural Network and its Application to Pattern Recognition. IEEE Transactions on Fuzzy Systems, vol. 2(3), pp. 185-193.

Lee, C. H. N., Liu, A., and Chen, W. S., 2006, Pattern Discovery of Fuzzy Time Series for Financial Prediction. IEEE Transactions on Knowledge and Data Engineering, vol. 18(5), pp. 613-625.

Liaw, A., and Weiner, M., 2002, Classification and Regression by Random Forests, R News, vol. 2(3), pp. 18-22.
URL: http://CRAN.R-project.org/doc/Rnews/

Lillesand, T. M., and Kiefer, R. W., Remote Sensing and Image Interpretation, Fourth Edition, John Wiley and Sons: New York, 2002, ISBN 9971-51-427-3.

Lu, D., Mausel, P., Batistella, M., and Moran, E., 2004, Comparison of land-cover classification methods of the Brazilian Amazon Basin. Photogrammetric Engineering and Remote Sensing, vol. 70, pp. 732-731.

Lu, D., and Weng, Q., 2007, A survey of image classification methods and techniques for improving classification performances. International Journal of Remote Sensing, vol. 28(5), pp. 823-870.

Lucieer, A., and Kraak, M., 2004, Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty. International Journal of Geographic Information Science, vol. 18, pp. 491-512.
Magnussen, S., Boudewyn, P., and Wulder, M., 2004, Contextual classification of Landsat TM images to forest inventory cover types. International Journal of Remote Sensing, vol. 25, pp. 2421-2440.

Mathur, A. and Foody, G. M., 2004, A relative evaluation of multiclass image classification by support vector machines. IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 1335-1343.

Mathur, A., and Foody, G. M., 2008a, Multiclass and binary SVM classification: Implications for training and classification users. IEEE Geoscience and Remote Sensing Letters, vol. 5, pp. 241–245.

Mathur, A. and Foody, G. M., 2008b, Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, vol. 29, pp. 2227-2240.

Mas, J. F., 2003, Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuarine, Coastal and Shelf Science, vol. 59(2004), pp. 219-230.

Melgani, F. and Bruzzone, L., 2004, Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, vol. 42, pp. 1778-1790.

Memarsadeghi, N., Mount, D. M., Netanyahu, N. S., and Moigne, J. L., 2003, A Fast Implementation of the ISOCLUS Algorithm. In Proceedings of the Geoscience and Remote Sensing Symposium, 2003. IGARSS '03, IEEE InternationalPublication, 21-25 July 2003, vol. 3, pp. 2057-2059.

Mingguo, Z., Qiangguo, C., and Mingzhou, Q., 2009, The Effect of Prior Probabilities in the Maximum Likelihood Classification on Individual Classes: A Theoretical Reasoning and Empirical Testing. Photogrammetric Engineering & Remote Sensing, vol. 75(9), pp. 1109-1116.

Na, X., Zhang, S., Li, Xiaofeng, Yu, H., and Liu, C., 2010, Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic mapper Imagery and Ancillary Geographic Data. Photogrammetric Engineering and Remote Sensing, vol. 76(7), pp. 833-840.

Oommen, T., Misra, D., Twarakavi, N. K. C., Prakash, A., Sahoo, B., and Bandopadhyay, S., 2008, An objective analysis of support vector machine based classification for remote sensing.Mathematical Geosciences, vol. 40, pp. 409-424.

Otukei, J. R., and Blaschke, T., 2010, Land Cover Change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, vol. 12S, pp. S27-S31.
Pal, M., and Mather, P. M., 2003, Support vector machines for classification in remote Sensing.International Journal of Remote Sensing, vol. 26, pp. 1007-1011.

PCI Geomatics Corp., ISOCLUS-Isodata clustering program. URL:http://www.pcigeomatics.com/cgi-bin/pcihlp/ISOCLUS

Plaza, A., Martinez, P., Perez, R., and Plaza, J., 2004, A Quantitative and Comparative Analysis of Endmember Extraction Algorithms From Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, vol. 42(3), pp. 650–663, 2004.

Piatetsky-Shapiro, G., and Frawley, W. J., editors. Knowledge discovery in databases. AAAI / MIT Press, Menlo Park, CA, 1991.

Piramuthu, S., 2006, Input Data for Decision Trees. Expert Systems with Applications, doi: 10.1016/j.eswa.2006.12.030.

Prasad, A. M., Iverson, L. R., and Liaw, A., 2006, Newer Classification and Regression Tree Techniques: bagging and Random Forests for Ecological Prediction. Ecosystem, vol. 9, pp. 181-199.

Quattrochi, D. A., and Goodchild, M. F., (Eds), Scale in Remote sensing and GIS, New York: Lewis Publishers, 1997.

Redner, E., and Walker, H., 1984, Mixture Densities, Maximum Likelihood and the EM Algorithm. SIAM Review,vol. 26(2), pp. 195-239.

Richards, J. A., and Jia, X., Remote Sensing Digital Image Analysis, Springer-Verlag: Berlin, 2006.

Rissanen, J., 1983, A Universal Prior for Integers and Estimation by Minimum Description Length. Annals of Statistics, vol. 11(2), pp. 417-431.

Rumelhart, D. E., Hinton, G. E., and Williams, R. J., 1986, Learning representations by back-propagating errors. Nature, vol. 323, pp. 533-535.

South, S., QI, J., and Lusch, D. P., 2004, Optimal classification methods for mapping agricultural tillage practices. Remote Sensing of Environment, vol. 91, pp. 90-97.

Strahler, A. H., 1980, The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, vol. 10, pp. 135–163.

Strahler, A. H., Woodcock, C. and Smith, J., 1986, On the nature of models in remote sensing. Remote Sensing of Environment, vol. 20, pp. 121-139.

Tou, J. T., and R. C. Gonzalez, Pattern Recognition principles, Addision-Wesley Publishing Company, Reading, Massachusetts, 1974.

Vapnik, W. N., 1999, An overview of statistical learning theory. IEEE Transactions of Neural Networks, vol. 10, pp. 988-999.

Vapnik, W. N., and Chervonekis, A. Y., 1971, On the uniform convergence of the relative frequencies of events to their probabilities. Theory of Probability and its Applications, vol. 17, pp. 265-280.

Velpuri, N. M., Thenkabail, P. S., Gumma, M. K., Biradar, C., Dheeravath, V., Noojipady, P., and Yuanjie, L., 2009, Influence of Resolution in Irrigated Area Mapping and Area Estimation. Photogrammetric Engineering & Remote Sensing, vol. 75, no. 12, pp. 1383-1395.

Venkatesh, Y. V., and KumarRaja, S., 2003, On the classification of multispectral satellite images using the multilayer perceptron. Pattern Recognition, vol.  36, pp. 2161-2175.

Walton, J. T., 2008, Subpixel Urban Land Cover Estimation: Comparing Cubist, Random Forests, and Support Vector Regression. Photogrammetric Engineering and Remote Sensing, vol. 74(10), pp. 1213-1222.

Waske, B., and Benediktsson, J. A., 2007, Fusion of Support Vector Machines for Classification of Multisensor Data. IEEE Transactions on Geoscience and Remote Sensing, vol. 45(12), pp. 3858-3866.

Watanachaturaporn, P., Varshney, P. K., and Arora, M. K., Evaluation of Factors Affecting Support Machines for Hyperspectral Classification, 2004.
URL:http://www.ecs.syr.edu/research/SensorFusionLab/Downloads/Pakorn/ASR S04_Evaluation_of_Factors_Affecting_SVM.pdf

Watanachaturaporn, P., Varshney, P. K. and Arora, M. K., 2007, Soft Classification using support vector machines regression. IEEE Transactions on Geoscience and Remote Sensing.

Watanachaturaporn, P., Varshney, P. K., and Arora, M. K., 2008, Multisource classification using support vector machines: an empirical comparison with decision tree and neural network classifiers. Photogrammetric Engineering and Remote Sensing, vol. 74(2), pp. 239-246.

Watts, J. D., and Lawrence, R. L., 2008, Merging Random Forest Classification with an object oriented approach for analysis of agricultural lands. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII, Part B7, Beijing, 2008.
URL: www.isprs.org/proceedings/XXXVII/congress/7_pdf/4.../18.pdf

Winter, M. E., 1999, N-Findr: an algorithm for fast autonomous spectral end-member determination in hyperspectral data. In Proceedings of the SPIE: Imaging Spectrometry, vol. 3753, pp. 266-275.

Wölfel, M., and Ekenel, H. K., 2005, Feature Weighted Mahalanobis Distance: Improved Robustness for Gaussian Classifiers, In Proceedings of the 13th European Signal Processing Conference:  EUSIPCO, Antalya, Turkey, September, 2005.
URL: http://www.eurasip.org/Proceedings/Eusipco/Eusipco2005/defevent/papers /cr1853 .pdf.

Woodcock, C. E., and Strahler, A., 1987, The factor of scale in remote sensing. Remote Sensing of Environment, vol. 21, pp. 311-332.

Wu, T-F., Lin, C.-J., and Weng, R. C., 2004, Probability estimates for multi-class classification by pairwise coupling. Journal of Machine Learning Research, vol. 5, pp. 975-1005. URL: http://www.csie.ntu.edu.tw/~cjlin/papers/svmprob/svmprob.pdf

Yang, X., 2011, Parameterizing Support Vector Machines for Land Cover Classification. Photogrammetric Engineering and Remote Sensing, vol. 77(1), pp. 27-37.

Yu, Q., Gong, P., Tian, Y. Q., Pu, R., and Yang, J., 2008, Factors Affecting Spatial Variation of Classification Uncertainity in an Image Object-based Vegetation Mapping. Photogrammetric Engineering and Remote Sensing, vol. 74(8), pp. 1007-1018.

Zheng, M., Cai, Q., and Wang, Z., 2005, Effect of prior probabilities on maximum likelihood classifier. Geoscience and Remote Sensing Symposium, 2005, IGARS’05, Proceedings 2005 IEEE International, vol. 6, pp. 3753-3756.

Zhou, L., and Yang, X., 2010, Training Algorithm Performance for Image Classification by Neural Networks. Photogrammetric Engineering and Remote Sensing, vol. 76(8), pp. 945-951.