Supervised Classification Approaches to Analyze Hyperspectral Dataset

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Sahar A. El Rahman 1,* Wateen A. Aliady 2 Nada I. Alrashed 2

1. Electrical Department, Faculty of Engineering-Shoubra, Benha University, Cairo, Egypt

2. Princess Noura bint AbdAlrahman University, Riyadh, KSA

* Corresponding author.


Received: 8 Jan. 2015 / Revised: 9 Feb. 2015 / Accepted: 4 Mar. 2015 / Published: 8 Apr. 2015

Index Terms

Atmospheric Correction, Hyperspectral image, Spectral Angle Mapper, Spectral Information Divergence, Supervised classification


In this paper, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classification approaches were used to classify hyperspectral image of Georgia, USA, using Environment of Visualizing Images (ENVI). It is a software application used to process and analyze geospatial imagery. Spatial, spectral subset and atmospheric correction have been performed for SAM and SID algorithms. Results showed that classification accuracy using the SAM approach was 72.67%, and SID classification accuracy was 73.12%. Whereas, the accuracy of SID approach is better than SAM approach. Consequently, the two approaches (SID and SAM) have proven to be accurately converged in classification of hyperspectral image of Georgia, USA.

Cite This Paper

Sahar A. El_Rahman, Wateen A. Aliady, Nada I. Alrashed,"Supervised Classification Approaches to Analyze Hyperspectral Dataset", IJIGSP, vol.7, no.5, pp.42-48, 2015. DOI: 10.5815/ijigsp.2015.05.05


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