A Semi Supervised based Hyper Spectral Image (HSI) Classification Using Machine Learning Approach
C. Rajinikanth1, S. Abraham Lincon2
1C. Rajinikanth, Research Scholar, Department of Electronics & Instrumentation Engineering, Annamalai University, (Tamil Nadu), India.
2Dr. S. Abraham Lincon, Professor, Department of Electronics & Instrumentation Engineering, Annamalai University, (Tamil Nadu), India.
Manuscript received on 05 February 2019 | Revised Manuscript received on 18 February 2019 | Manuscript Published on 04 March 2019 | PP: 13-16 | Volume-7 Issue-5S2 January 2019 | Retrieval Number: ES1998017519/19©BEIESP
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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: In this paper, a new algorithm has been designated for classification of satellite remote sensing of hyperspectral image. The classification process is based on the three main categories: filtering, Clustering and classified, in this process to achieve a new optimal image clustering to overcome the problem of multi-label images in satellite remote processing. Finally, it gets clustered and result in classified output. The proposed research contribution is validated by classification experiments using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image sensors from the results the overall accuracy of single and multi-label of Salinas A dataset.
Keywords: Hyperspectral Image, Clustering, Classification and Optimization.
Scope of the Article: Machine Learning