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by GLCM, and correlations between them, which has been analyzed

above. The textural measure of Entropy based fusion has more spatial

information, and less spectral distortion. Homogeneity based fusion

has the least information, and Second moment based fusion has the

largest Spectral distortions. So when we fuse textural measures with

images, it is important to choose the textural measures.

Then the evaluation index, Shannon entropy, sharpness, spectral

distortion and correlation coefficient were applied to evaluate the

fusion results. From Table 1, we can see that the information

abundance of fusion with single textural measure are all increased.

That means it is reasonable to fuse texture with images. But the

results are close to each other. That is because they are all obtained

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012

XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia

"exture measures Sharpness Shannon Spectral correlation

entropy distortions coefficient

Mean 7.3447 1.1949 25.0928 0.7536

Variance 6.2519 1.1270 24.5272 0.7387

Homogeneity 6.9448 0.9866 27.9070 0.7809

Contrast 6.2741 1.1281 24.4223 0.7399

Dissimilarity 7.5511 1.1983 24.6145 0.7624

Entropy 8.2487 1.2574 24.9710 0.7811

Second moment 8.2528 1.0632 33.3779 0.7151

Original QuickBird image 10.6940 0.9332 --- -—

Table 1. Evaluation of PCA fusion based on texture analysis

5. CONCLUSION

The results of the procedure proposed in this letter show that it is

possible to extract some kind of texture information high satellite

SAR images and fuse them with some methods. In this study, texture

has been extracted by GLCM after analysis, and fusion performed

simply by PCA at pixel level. The results are not so satisfactory. That

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