Hyper Spectral Image Segmentation and Classification Using Least Square Clustering Based on FODPSO

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Jha Soni Vinit, Prof. Sachin Bojewar, Prof. Deepali Patil

Abstract

The spatial analysis of the image detected and acquired by a satellite provides less accurate information on a remote location. Hyperspectral images are one of the images detected remotely, they are superior to multispectral images that provide spectral information. detailed information is one of the important requirements in many areas, such as military, agriculture, etc. The FODPSO classifier algorithm is used with the grouping technique of least squares for image segmentation. The 2D adaptive filter is proposed to eliminate the noise of the hyperspectral image detected and captured in order to eliminate the noise of the spot. Denoising the hyperspectral image (HSI) is an essential pre-processing step to improve the performance of subsequent applications.

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How to Cite
, J. S. V. P. S. B. P. D. P. “Hyper Spectral Image Segmentation and Classification Using Least Square Clustering Based on FODPSO”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 6, no. 1, Jan. 2018, pp. 83 -, doi:10.17762/ijritcc.v6i1.1384.
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