A supervised Multi-Spectral Image Classification for Remote Sensing Data

DOI

10.2991/racs-15.2016.20How to use a DOI?

Keywords

Minimum Distance (MD), Maximum Likelihood (ML), Probabilistic Neural Network (PNN), Principal Component Analysis (PCA), False Colour Composite (FCC).

Abstract

With the advent of photography equipment and techniques combination to revolution of computer and digitalization in both hardware and software, it takes another dimension. This research shade some light on the Multi-Spectral Image Classification and the importance of this field in Image processing. The supervised classification approach was considered in this research where three of its types were explained, Minimum Distance (MD), Maximum Likelihood (ML), and Probabilistic Neural Network (PNN). The research involves designing a package for Multi-Spectral Image classification. This includes reading data, apply Principal Component Analysis (PCA) as a feature extraction, then apply False Colour Composite (FCC) as one of the classification techniques in multi-spectral images. The research focuses on the supervised method throughout.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Akram Zeki
AU  - Muhsin Zaid
PY  - 2015/11
DA  - 2015/11
TI  - A supervised Multi-Spectral Image Classification for Remote Sensing Data
BT  - Proceedings of the 2015 International Conference on Recent Advances in Computer Systems
PB  - Atlantis Press
SP  - 119
EP  - 123
SN  - 2352-538X
UR  - https://doi.org/10.2991/racs-15.2016.20
DO  - 10.2991/racs-15.2016.20
ID  - Zeki2015/11
ER  -