Supervised, Unsupervised, and Semisupervised Classification Methods for Hyperspectral Image Classification-A Review

Abstract

Remote sensing involves collection and interpretation of information about an object, area or event without any physical contact with the object. All earth surfaces features which include minerals, vegetation, dry soil, water and snow have unique spectral reflectance signatures. These spectral signatures vary over the range of wavelengths in the electromagnetic spectrum and these all large number of signatures is correctly identified with hyperspectral images. Accurate classification of hyperspectral image is an evolving field in now days. In this section we give a wide outline of existing methodologies focused around supervised, unsupervised and semisupervised hyperspectal image classification methods and some well known applications of hypergraph.

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Cite this paper

@inproceedings{Sabale2014SupervisedUA, title={Supervised, Unsupervised, and Semisupervised Classification Methods for Hyperspectral Image Classification-A Review}, author={Savita P. Sabale and Chhaya R. Jadhav}, year={2014} }