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Band Selection in Hyperspectral Images using Independent Component Analysis

Authors(1):

Vinay Chandragiri
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In Hyperspectral Imaging is the new modality in medical applications which is probably being used in Remote sensing applications. The image is generally of high dimension with spectral bands for a pixel. The main idea of the segmentation is to identify cancerous cells among the tissues. Here I am trying to address the problem of classifying cells by gland segmentation for cancer detection in the given colon tissue. The dimensionality problem has been tackled by Band Selection based on Independent Component Analysis.

Vinay Chandragiri

Hyperspectral Imaging, ICA, Clustering

  1. Independent Component Analysis: Algorithms and Applications Aapo Hyvrinen and Erkki Oja Neural Networks Research Centre Helsinki University of Technology, Finland
  2. Spatial Mutual Information Based Hyperspectral Band Selection for Classification
  3. A Comparative Analysis of Dimension Reduction Algorithms on Hyperspectral Data Kate Burgers, Yohannes Fessehatsion, Jia Yin Seo, Sheida Rahmani

Publication Details

Published in : Volume 2 | Issue 5 | September-October - 2016
Date of Publication Print ISSN Online ISSN
2016-10-30 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
188-191 IJSRSET162548   Technoscience Academy

Cite This Article

Vinay Chandragiri, "Band Selection in Hyperspectral Images using Independent Component Analysis", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 5, pp.188-191, September-October-2016.
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