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

Savita P. Sabale, Asst. Prof. Chhaya R. Jadhav

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 semi-supervised hyperspectal image classification methods and some well known applications of hypergraph

Keywords: High Dimensionality, Lack of Training Samples, Supervised Classification Method, Unsupervised Classification Method, Semisupervised Classification Method, Applications of Hypergraph

How to Cite?: Savita P. Sabale, Asst. Prof. Chhaya R. Jadhav, "Supervised, Unsupervised, and Semisupervised Classification Methods for Hyperspectral Image Classification-A Review", Volume 3 Issue 12, December 2014, International Journal of Science and Research (IJSR), Pages: 256-260, https://www.ijsr.net/getabstract.php?paperid=SUB14368, DOI: https://dx.doi.org/10.21275/SUB14368

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