Design and Development of Hyper Spectral Image Classification Using Enhanced Mean Shift Segmentation in Image Mining
Journal Title: International journal of Emerging Trends in Science and Technology - Year 2017, Vol 4, Issue 8
Abstract
Hyper spectral imaging is becoming an important analytical tool for generating land-use map. High dimensionality in hyper spectral remote sensing data can guaranty in principle a detailed discrimination of the observed surfaces overcoming the intrinsic limitation of lower spectral resolution data. We propose an ovel approach for solving the perceptual grouping problem in vision. In the existing methods the image segmentation was done by using special spectral classification. Special spectral may have some of the problems such as quality and clarity of the particular image. We show that an efficient computational technique based on a generalized region value problem can be used to optimize this criterion. In our proposed method Enhanced mean shift algorithm is used for segmenting the part of the particular image. Experimental result use accuracy and execution time parameters to show the performance. It takes low computational complexity and also accurate result for real-time image segmentation processing
Authors and Affiliations
Dr. S. Thavamani
Keywords
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- EP ID EP246008
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