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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.