Abstract: Hyperspectral Imaging (HSI) is used to provide a wealth of information which can be used to address a variety of problems in different applications. The main requirement in all applications is the classification of HSI data. In this paper, supervised HSI classification algorithms are used to extract agriculture areas that specialize in wheat growing and get a classified image. In particular, Parallelepiped and Spectral Angel Mapper (SAM) algorithms are used. They are implemented by a software tool used to analyse and process geospatial images that is an Environment of Visualizing Images (ENVI). They are applied on Al-Kharj, Saudi Arabia as the study area. The overall accuracy after applying the algorithms on the image of the study area for SAM classification was 66.67%, and 33.33% for Parallelepiped classification. Therefore, SAM algorithm has provided a better a study area image classification.

Sahar A. El_Rahman, “Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image” International Journal of Advanced Computer Science and Applications(IJACSA), 7(5), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070509

@article{El_Rahman2016,
title = {Performance of Spectral Angle Mapper and Parallelepiped Classifiers in Agriculture Hyperspectral Image},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070509},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070509},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {5},
author = {Sahar A. El_Rahman}
}