An Efficient Spectral Spatial Classification for Hyper Spectral Images


  • G Shanmugapriya
  • . .





Extended random walkers, hyper spectral images, optimization, spectral-spatial classification, k-means.


An expanded random walker comprises of two primary advances ghostly spatial order strategy for hyper Ghastly pictures. To begin with go to pixel astute order by utilizing bolster vector machine (SVM) which is arrangement likelihood maps for a hyper unearthly picture. Probabilities of hyper phantom Pixel have a place with various classes. The second approach is getting pixel shrewd likelihood maps are upgraded broadened arbitrary walker calculation. Pixel astute measurements data by SVM classifier, spatial relationship between neighboring pixels displayed through weight of diagram edges preparation and test tests demonstrated irregular walkers. These 3 components utilizing for the class of validating pixel are resolved. So, these three elements considered in ERW. The proposed technique demonstrates great order performs for three generally utilized genuine hyper otherworldly informational collections even the quantity of preparing tests is moderately little.




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How to Cite

Shanmugapriya, G., & ., . (2018). An Efficient Spectral Spatial Classification for Hyper Spectral Images. International Journal of Engineering & Technology, 7(3.12), 1050–1052.
Received 2018-08-16
Accepted 2018-08-16
Published 2018-07-20