International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
5. CONCLUSION 
The high spectral resolution of hyperspectral data provides the 
ability for diagnostic identification of different materials. In 
order to analyze such hyperspectral data by using the current 
techniques and to increase the classification performance, 
dimension reduction is pre-processing for removing the 
redundant information substantially without sacrificing 
significant. information and of course preserving the 
characteristics of the spectral signature. In this paper, we have 
presented an efficient dimension reduction technique for 
hyperspectral data based on automatic Wavelet decomposition. 
With a high number of bands produced from hyperspectral 
sensors, we showed that the Wavelet Reduction method yields 
similar or better classification accuracy than PCA. This can be 
explained by the fact that Wavelet reduced data represent a 
spectral distribution similar to the original distribution, but in a 
compressed form. Keeping only the approximation after 
Wavelet transform is a lossy compression as the removed high 
frequency signal (details) may contain useful information for 
class separation and identification. PCA also has a similar 
problem when not all the components are kept. This is, 
however the tradeoff when compression or reduction is used. 
REFRENCES 
[1] Fukunaga, K., 1989, “Effect of Sample Size in Classifier 
Design,” IEEE Pattern Analysis and Machine Intelligence, Vol. 
11, No 8, pp. 873-885. 
[2] Hsieh, P.F., and Landgrebe, D.A., 1998 “Classification of 
High Dimensional Data”, -School of Electrical and Computer 
Engineering, Purdue University, West Lafayette, Indiana, USA. 
[31 HSU, PH, 
Analysis of Hyperspectral 
and TSENG, Y.H., 2000, “Wavelet Based 
Data for Detecting Spectral 
65 
Features,” International Archives of Photogrammetry and 
Remote Sensing .Vol. XXXIII, Supplement B7. Amsterdam. 
[4] Ifarraguerri,A., and Chang, C.1.,2000 “Unsupervised 
Hyperspectral Image Analysis With Projection Pursuit,” IEEE 
Geosci.Remote Sensing, Vol.38, No. 6, November 2000. 
[5] KaewpijiS., Moigne,JL. and EL-Ghazawi,T.,2003 
“Automatic Reduction of Hyperspectral Imagery Using 
Wavelet Spectral Analysis,” IEEE Geosci. Remote Sensing, 
Vol.41, No.4. 
[6] Mathur, A., 2002, “Dimensionality Reduction of 
Hyperspectral Signatures Detection of Invasive Species”, 
Department of Electrical and Computer Engineering, 
Mississippi State University, Mississippi. 
[7] Mallat, S.G., 1989 “A theory for multiresolution signal 
decomposition: The wavelet representation,” [EEE Pattern 
Anal. Machine Intell., vol. 11, pp. 674—693. 
[8] Richards, J.A. 1993 Remote Sensing Digital Image 
Analysis: An Introduction, 2nd Ed. New York: Springer-Verlag. 
[9] Salehi, B., Valadan Zouj M.J, 2004,"Studying the 
Analyzing Methods for Classification of Hyperspectral 
Imagery", Faculty of Geodesy & Geomatics Engineering, 
K.N.T. University of Technology, Tehran, Iran . 
[10] Swain, P.H., and Davis, S.M., 1978, Remote Sensing: The 
Quantitative Approach. New York: McGraw-Hill.