Assimilation of Standard Regularizer Contextual Model and Composite Kernel with Fuzzy-based Noise Classifier

Abstract

The paper assay the effect of assimilating smoothness prior contextual model and composite kernel function with fuzzy based noise classifier using remote sensing data. The concept of the composite kernel has been taken by fusing two kernels together to improve the classification accuracy. Gaussian and Sigmoid kernel functions have opted for kernel composition. As a contextual model, Markov Random Field (MRF) Standard regularization model (smoothness prior) has been studied with the composite kernel-based Noise Classifier. Comparative analysis of new classifier with the conventional construes increase in overall accuracy.

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

“…This paper is the extension of the previous work done related kernel based noise classifier, KNC, using nine-kernel function in supervised model [16,17]. The objective of present paper is to assess the associativity of untrained class upon kernel based noise classifier.…”