Classification of remote sensing image using SVM kernels

With reference to the literature worldwide, it is obvious that Support Vector Machine (SVM), a machine learning algorithm has proven records for excellent results regarding Classification of Image. But, Remote Sensing Images are considered as most complex in nature as far as classification is concern. Supervised classification of Remote Sensing Images needs more precise machine learning models, which will be fast and efficient. SVM do satisfy researchers all over the world as far as Remote Sensing Images are concern. Basically, SVM is non-parametric statistical learning based model, which acts like binary classifier. SVM represents a group of superior machine learning algorithms, where it decomposes the parameter of the problem into a quadratic optimization technique. Hence, SVM is used to locate optimum boundaries between classes, which in return generalize to unseen samples with least error among all possible boundaries separating two classes. SVM uses density estimation function for developing easy and efficient learning parameters. Like other supervised algorithms, SVM also undergo into Training, Learning and Testing Phase for classifying any image. Besides all parameters, training sample selection and optimization is crucial part that affects the classification accuracy of remote sensing images. We need to address this issue in our project so as to devise noble algorithm or approach, which could make SVM, a more robust statistical learning model.