Band Selection in Hyperspectral Images using Independent Component Analysis

Authors

  • Vinay Chandragiri  Department of Computer Science and Engineering, IIT Guwahati, Guwahati, Assam, India

Keywords:

Hyperspectral Imaging, ICA, Clustering

Abstract

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.

References

  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

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2016-10-30

Issue

Volume 2, Issue 5, September-October-2016

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
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.