Hello visitor, its been a while since my last post. Starting from now, I will try to post my blog in English language. This is hard for me because I neither English nor Americans. I am Indonesian. I hope you can understand if my grammar or structure maybe very confusing. Please pardon me. I still have to learn. Ok back to the topic, few days ago, I had downloaded the trial/demo version of Leica Photogrammetry Suite version 9.2 from their FTP (ftp.gi-leicageosystems.com). After exploring some new modules, I get excite with a new image fusion/pan sharpening algorithm that as far I know, not implemented in previous IMAGINE or LPS version. The algorithm was developed by Mr Manfred Ehlers from Onasbruck University Germany, and it is called Ehlers fusion. I have tried to use it to fuse quickbird 16 BIT sample data from digitalglobe (multispectral and panchromatic), and the result is awesome. Comparison between original dataset and sharpening result indicated that this algorithm can maintain spectral integrity from original multispectral dataset without losing spatial details from panchromatic dataset. Looking the result, I have idea to compare this algorithm with Mr Yun Zhang PANSHARP algorithm that now implemented in PCI Geomatica (www.pcigeomatics.com), and Gram-schmidt Spectral Sharpening that now implemented in ITT ENVI (www.ittvis.com). The reason why I chosen PANSHARP and Gram-schmidt is both algorithms are the best pan sharpening algorithms I currently know. These algorithms can preserve either spectral integrity from multispectral bands or spatial details from panchromatic band. The picture below is the comparison result that visualized on ERDAS IMAGINE GLT viewer.
From the picture, we can see that Ehlers fusion giving superior spatial details than Gram-schmidt (GS) and PANSHARP. GS and PANSHARP still leave little artifacts (spectral residuals from original multispectral dataset, especially in bright objects) on the sharpened imagery whereas in the ehlers fusion result, they re totally blended.
I have tried to assess the fusion results using correlation analysis between original multispectral bands and the sharpened bands. Below are the scatter plot results from band 1 (VIS Blue) comparison.
And below are the correlation analysis results.
band x (original bands) band y (sharpened bands) Linear regression equation r ori_1 YZ_1 0,96x + 10,37 0,96 ori_2 YZ_2 0,96x + 14,92 0,96 ori_3 YZ_3 0,96x + 10,9 0,96 ori_4 YZ_4 0,96x + 14,16 0,96 ori_1 ehlers_1 0,83x + 84,54 0,83 ori_2 ehlers_2 0,88x + 41,99 0,89 ori_3 ehlers_3 0,93x + 9,29 0,95 ori_4 ehlers_4 0,97x + 11,44 0,97 ori_1 GS_1 0,90x + 23,34 0,94 ori_2 GS_2 0,91x + 32,04 0,94 ori_3 GS_3 0,94x + 15,66 0,95 ori_4 GS_4 0,99x + 4,57 0,96
Explanation : 1 = band 1 (blue); 2 = band 2 (green ); 3 = band 3 (red); 4 = band 4 (NIR)
Correlation analysis results showed that Yun Zhang PANSHARP algorithm give stable spectral preservation at all bands. Opposite with the spatial quality analysis result, Ehlers fusion can’t maintain the spectral preservation at all bands than GS or YZ PANSHARP. Although at band 4 (NIR) Ehlers give the best correlation with the original band, the correlation was dropped on the other bands. Although some algorithm give better result than the others, we can concluded that all the three algorithms give satisfied spectral preservation result and high correlation coefficient with the original multispectral bands. These algorithms are better than the more traditional algorithms like IHS fusion, CN Spectral sharpening, Brovey, and PC fusion.