Comparative Study of Algorithms for Hyper Spectral Image Classification

Authors

  • Pallavi Juyal  Department Computer Engineering, Mukesh Patel School of Technology Management and Engineering, Mumbai, Maharashtra, India
  • Manish Jain  Department Computer Engineering, Mukesh Patel School of Technology Management and Engineering, Mumbai, Maharashtra, India
  • Varsha Nemade  

Keywords:

Remote Sensing, Hyperspectral Image, Classification, Classifier, Spectrum, Electromagnetic Spectrum, Euclidian Distance.

Abstract

Now days the use of remote sensing imagery has increased drastically, ranging from the applications in farms to that in the field of defense. These images range from hyper spectral, multispectral to ultra spectral. One of the major applications of the remote sensing image is that of classification of the image. Several algorithms have been found for classification of images based on various factors [4]. The algorithms used for classification are either supervised or unsupervised. In this paper we study different algorithms used for classification of the hyper spectral images. An analysis of the output obtained by the implementation of various algorithms has also been done. The analysis has been done based on the number of pixels classified.

References

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2017-04-30

Issue

Volume 2, Issue 2, March-April-2017

Section

Research Articles

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

How to Cite

[1]
Pallavi Juyal, Manish Jain, Varsha Nemade, " Comparative Study of Algorithms for Hyper Spectral Image Classification , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.1000-1004, March-April-2017.