A Tutorial on Different Classification Techniques for Remotely Sensed Imagery Datasets

Classification techniques are used on large databases to develop models describing different data classes. Such analysis can provide deep insight for better understanding of different large-scale databases. Studies related to knowledge acquisition and effective knowledge development are also very popular in the remote sensing field with satellite imagery datasets. In any remote sensing research, the decision-making process mainly depends on the effectiveness of the classification process. Efficient classification techniques were developed and applied to the Statlog (Landsat Satellite) database at the University of California, Irvine Machine Learning Repository to identify six land type classes. We used three different classification algorithms on the large satellite imagery: multilayer perceptron backpropagation neural network (MLP BPNN), support vector machine (SVM), and k-nearest neighbor (k-NN). This research study aimed to evaluate the performance of these classification algorithms in the prediction of the classified lands from this large set of satellite imagery. We used different performance measures, such as classification accuracy, root-mean-square error, kappa statistic, true positive rate, false positive rate, precision, recall, and F-measure to evaluate the performance of each classifier. Among the three classification techniques applied, MLP BPNN turned out to be the best; next was k-NN, followed by SVM.

[1]  Philip S. Yu,et al.  Data Mining: An Overview from a Database Perspective , 1996, IEEE Trans. Knowl. Data Eng..

[2]  Stephen V. Stehman,et al.  Selecting and interpreting measures of thematic classification accuracy , 1997 .

[3]  A. Karnieli,et al.  Comparison of methods for land-use classification incorporating remote sensing and GIS inputs , 2011 .

[4]  Huan Liu,et al.  Book review: Machine Learning, Neural and Statistical Classification Edited by D. Michie, D.J. Spiegelhalter and C.C. Taylor (Ellis Horwood Limited, 1994) , 1996, SGAR.

[5]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[6]  V. Ramesh,et al.  Classification of Agricultural Land Soils: A Data Mining Approach , 2011 .

[7]  David J. Spiegelhalter,et al.  Machine Learning, Neural and Statistical Classification , 2009 .

[8]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[9]  Trevor Darrell,et al.  Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing) , 2006 .

[10]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[11]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[12]  Kaushik Deb,et al.  A Motion Region Detection and Tracking Method , 2014, Smart Comput. Rev..

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  Jee-Hyong Lee,et al.  Multi-Intention Web Pages Model Based on User Keywords of Interest , 2014, Smart Comput. Rev..

[15]  T. W. Anderson.  An Introduction to Multivariate Statistical Analysis , 1959 .

[16]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[17]  Qihao Weng,et al.  A survey of image classification methods and techniques for improving classification performance , 2007 .

[18]  Jean Carletta,et al.  Assessing Agreement on Classification Tasks: The Kappa Statistic , 1996, CL.

[19]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..