Enhancing Spectral Classification Using Adaboost

Saipullah, Khairul Muzzammil (2012) Enhancing Spectral Classification Using Adaboost. In: 2012 IEEE Asia-Pacific Conference on Applied Electromagnetics (APACE) 2012, 11 - 13 Dec 2012, Melaka.

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Spectral classification for hyperspectral image is a challenging job because of the number of spectral in a hyperspectral image and high dimensional spectral. In this paper, we proposed a method to enhance the spectral classification using the Adaboost for hyperspectral image analysis. By applying the Adaboost algorithm to the classifier, the classification can be executed iteratively by giving weight to the spectral data, thus will reduce the classification error rate. The Adaboost is implemented to spectral angle mapper (SAM),Euclidean distance (ED), and city block distance (CD). From the experimental results, the Adaboost increases the average classification accuracy of 2000 spectral up to 99.63% using the CD. Overall, Adaboost increases the average classification accuracy of ED, CD, and SAM by 2.54%, 1.95%, and 1.67%.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Electronics and Computer Engineering > Department of Computer Engineering
Depositing User: Engr. Khairul Muzzammil Saipullah
Date Deposited: 15 Jul 2013 03:47
Last Modified: 28 May 2015 03:57
URI: http://eprints.utem.edu.my/id/eprint/8553
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