Examination of Kernel Based Noise Classifier with Cross Resolution Dataset and with Untrained Class
Ishuita Sen Gupta1, Anil Kumar2, Rakesh Kumar Dwivedi3
1Ishuita SenGupta*, College of Computing Sciences and Information Technology, Teerthanker Mahaveer University, Moradabad, India.
2Anil Kumar, Indian Institute of Remote Sensing, ISRO, Dehradun, India.
3Rakesh Kumar Dwivedi, College of Computing Sciences and Information Technology, Teerthanker Mahaveer University, Moradabad, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3389-3394 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6539018520/2020©BEIESP | DOI: 10.35940/ijrte.E6539.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Kernel functions, Noise Clustering Classifier, Cross Resolution, and Untrained Classes.
Scope of the Article: Clustering.