Examination of Kernel Based Noise Classifier with Cross Resolution Dataset and with Untrained Class

Abstract

Determining the effect of untrained classes in the kernel based noise classifier is the prime object of the paper. It further includes, studying the effect of studied classifier over different datasets. Distinct nine Kernel functions has been associated with conventional supervised Noise Classifier. Landsat8 and Formosat2 along with Resourcesat-1 data have been opted for the performance evaluation. Decrease in classification accuracy has been found, in presence of untrained classes. A subtle consistency has been in classification accuracy in case of cross resolution data sets, thus, showing the robustness of the algorithm.