Hyper spectral Image Classification and Unfixing by using ART and SUNSPI Techniques
Nagarajan Munusamy1, Rashmi. P. Karchi2
1Nagarajan Munusamy, Department of Multimedia and Web Technology, KSG College of Arts and Science, Coimbatore – 641015, (Tamil Nadu), India.
2Rashmi. P.Karchi, Department of Computer Science, Bharathiar University, Coimbatore-641046, (Tamil Nadu), India.

Manuscript received on 18 April 2019 | Revised Manuscript received on 26 May 2019 | Manuscript published on 30 May 2019 | PP: 777-784 | Volume-8 Issue-1, May 2019 | Retrieval Number: A9109058119/19©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: The Hyperspectral images extracts, collects and processes the information from across the electromagnetic spectrum. The main aim of the hyperspectral imaging is to get the spectrum from each pixel in the images, in the purpose to find the objects, materials, or detecting processes. The spectral range in the hyperspectral images gives the ability to identify chemical types on the environment of Mars more precisely than before. For extracting the hidden features in the mixed pixel, demonstrating state-of-the-art presentation when evaluate with freshly established hyper spectral image classification techniques. Then proposed method is experimentally calculated by using both pretended and actual hyperspectral datasets. The integration of Unmixing algorithm termed “Sparse Unmixing of Hyperspectral information with Spectral a Priori data” with the Singular Spectrum Analysis approach, to get the better result the Clustering by “Adaptively Regularized Kernel-Based Fuzzy C-Means” and Segmentation with “Watershed” of images is carried out and for the better level of classification is done using the ART classifier. The integration of these methods signifies an innovative contribution in the research field of hyperspectral imagery.
Index Terms: Clustering and Segmentation, Hyperspectral Image Classification, Mixed Pixel, Unmixing.

Scope of the Article: Classification