Enhancing spectral classification using Adaboost

Author

Saipullah, K.M. ; Ismail, Nur Ain

Author_Institution

Fac. of Electron. & Comput. Eng., Univ. Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

fYear

2012

fDate

11-13 Dec. 2012

Firstpage

17

Lastpage

21

Abstract

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%.

Keywords

geographic information systems; hyperspectral imaging; image classification; learning (artificial intelligence); remote sensing; Adaboost; Euclidean distance; city block distance; hyperspectral image analysis; spectral angle mapper; spectral classification; Accuracy; Classification algorithms; Euclidean distance; Hyperspectral imaging; Support vector machine classification; Training; Adaboost; SAM; hyperspectral; spectral classification; spectral similarity;

fLanguage

English

Publisher

ieee

Conference_Titel

Applied Electromagnetics (APACE), 2012 IEEE Asia-Pacific Conference on

Conference_Location

Melaka

Print_ISBN

978-1-4673-3114-2

Type

conf

DOI

10.1109/APACE.2012.6457623

Filename

6457623