Wavelet Spectral Analysis for Automatic Reduction of Hyperspectral Imagery

Wavelet Spectral Analysis for Automatic Reduction of Hyperspectral Imagery

Data Source and Study Area
In this work we used an image of a portion of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) of hyperspectral data taken over an agricultural area of California, USA in 1994 (Fig.3). This image has 195 spectral bands about 10nm apart in the spectral region from 0.4 to 2.45µm with a spatial resolution of 20m.The test image has a pixel of 145 rows by 145 columns. And its corresponding ground truth map is involving 12 class. The number of training pixel for each class is in Table2.

Fig.3. Test AVIRIS data. California 1994
Experimental results
In this work we used two software for implementation of our research.

MATLAB 6.5: This software has been used for implementation of Wavelet reduction and PCA algorithms.

ENVI 3.5: This software also was used for assessing the effect of Wavelet reduction algorithm in overall accuracy of different classification method compare to PCA. The overall classification accuracies obtained from both of dimension reduction methods are listed in Table4. As shown in Table4 for ML algorithm the Wavelet reduction gives 95.73% overall accuracy for the first level of decomposition, while PCA only gives 95.3% .The same trend is seen for MB classification method and for all level of decomposition.

The two other classification methods (MD and PP), are sometimes chose over the ML classification because of their speeds. Yet they are known to be much less accurate than the ML classification.

Some authors believe that there are two main factors that make Automatic Wavelet Reduction outperform the PCA as follows.

The nature of classification, which are mostly pixel-based techniques and are thus well suited for Wavelet, which is pixel-based transformation.
The lowpass and some of highpass portions of the remaining information content, not includes in the firsts PCs, are still present in the Wavelet reduced data.

Table3. Classification result from comparing PCA and Wavelet Reduction

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.

References

J.A. Richards, Remote Sensing Digital Image Analysis: An Introduction, 2 nd Ed. New York: Springer-Verlargg, 1993.
S.Kaewpijit, J.L.Moigne and T.EL-Ghazawi, “Automatic Reduction of Hyperspectral Imagery Using Wavelet Spectral Analysis,” IEEE Geosci. Remote Sensing, Vol.41, No.4, April 2003.
L.O.Jimenez and D.Landgrebe, “High dimensional Feature Reduction Via Projection Pursuit, ” School of Electrical and Computer Engineering Purdue University West Lafayette, April 1995.
A.Ifarraguerri and C.I.Chang, “Unsupervised Hyperspectral Image Analysis With Projection Pursuit,”IEEE Geosci.Remote Sensing, Vol.38, No. 6, November 2000.
P.H. HSU and Y.H.TSENG, “Wavelet Based Analysis of Hyperspectral Data for Detecting Spectral Features,” International Archives of Photogrammetry and Remote Sensing .Vol.XXXIII, Supplement B7. Amsterdam 2000.
K.Fukunaga, “Effect of Sample Size in Classifier Design,” IEEE Pattern Analysis and Machine Intelligence,Vol. 11, No 8 ,pp.873-885,1989.
H.Emami, “Evaluation and Decomposition of Mixed Pixel in Remotely Sensed Images for Accuracy Improvement of Classification Results”,A thesis for MS. Degree at K.N.Toosi University of Technology,Tehran,Iran,2002.

Page 4 of 4
| Previous |