Kernel based learning approach for satellite image classification using support vector machine

Machine learning is a scientific computing discipline to automatically learn to recognize complex patterns and make intelligent decisions based on the set of observed examples (training data). Support Vector Machine (SVM) is a supervised machine learning method used for classification. An SVM kernel based algorithm builds a model for transforming a low dimension feature space into high dimension feature space to find the maximum margin between the classes. In the field of geospatial data processing, there is a high degree of interest to find an optimal image classifier technique. Many image classification methods such as maximum likelihood, K-Nearest are being used for determining crop patterns, land use and mining other useful geospatial information. But SVM is now considered to be one of the powerful kernel based classifier that can be adopted for resolving classification problems. The objective of the study is to use SVM technique for classifying multi spectral satellite image dataset and compare the overall accuracy with the conventional image classification method. LISS-3 and AWIFS sensors data from Resourcesat-1, Indian Remote Sensing (IRS) platform were used for this analysis. In this study, some of the open source tools were used to find out whether SVM can be a potential classification technique for high performance satellite image classification.