3. Experimental Results
A JERS-1/OPS image, size of 256 x 256 pixel, was used in this experiment. Figure 4 shows the first principal component image of KLT to be compressed. The reconstructed images resulted by the wavelet transform method and the proposed method, when the wavelet coefficients were quantized to 2,4 and 8 levels, are shown in Figure 5. The quality of the reconstructed images is measured in terms of MSE and PSNER, and the results are given in Table 1. We can see that the proposed method is superior to the classical wavelet-based method.

Figure 4: first principal component image.

Quantization Level	MSE		PSNR
	Wavelet	MDPCM-WT	Wavelet	MDPCM-WT
2 levels	1095.2	80.7	65.9	77.2
4 levels	401.2	34.3	70.2	80.9
8 levels	140.2	32.2	74.8	81.2
256 levels	1.3	0.0	95.3	358.4

Table 1: Quantitative comparison between the classical wavelet and the proposed methods.
4. Conclusion
A lossy compression algorithm for multispectral remote-sensing was described. We have shown that applying median-predictor DPCM on wavelet coefficients can improve the image quality when compared to the classical wavelet-based method at the same bit rate.
Figure 5: Reconstructed images resulted by the classical wavelet transform method (a,c,e) and by the proposed method (b,d,f) at various quantization levels. (a-b) 8-levels, (c-d) 4-levels, and (e-f) 2-levels.
Acknowledgement
The authors wish to thank the National Research council of Thailand (NRCT) for providing the satellite image data.

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