, SPOT, IRS, Landsat7, IKONOS, QuickB ird O rbv iew. 4 : W avelet, , ( IFOV ) IFOV, , IKONOS QuickB ird Pan. Landsat TM SPOT HRV MS

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1 GEO2INFORMATION SC IENCE Vol110, No14 Aug1, , 2, 1 (1, ; 2, ) :, W avelet SF IM Gram2schm idt PC I pansharpquickb ird :,,, QuickB ird : ; ; QuickB ird 1, SPOT, IRS, Landsat7, IKONOS, QuickB ird O rbv iew,, [ 1, 2 ] (1 ),,, ( IFOV ) ; IFOV,, ( 2),,,, IKONOS QuickB ird, [ 3 ] H IS,, BROVEY, SPOT/ IRS Pan Landsat TM SPOT HRV MS, IKONOS QuickB ird Pan,, QuickB ird, 4 : W avelet, SF IM, Gram2schm idt PC I pansharp, 2 211,,,, 1, : ; : : (2008J0242) ; : ( ),,, E2mail: lchfzfj@ yahoo1com1cn

2 4 : 521 [ 4 ] ( 2007),, [ 5 ] 1 Tab11 In terpreta tion of rad iom etr ic ca libra tion I QuickB ird Pan, H IS, QuickB ird Pan IHS I,, 212 H IS,, BROVEY SPOT Pan MS Landsat TM,, IKONOS QuickB ird Pan MS Z1Yun (2004) [ 3 ] 2 SPOT5 (HRV ) Landsat7 ( ETM + ) IKONOS QuickB ird Pan 2 Pan Tab12 W avelength ranges of d ifferen t panchroma tic images SPOT5 (HRV) Landsat7 ( ETM + ) IKONOS QuickB ird Pan ( m) SPOT5 (HRV ), Landsat7 ( ETM + ) IKONOS QuickB ird Pan IHS, Landsat7 ( ETM + ) IKONOS QuickB ird Pan I SPOT Pan I 3,,, ;,, 311 (1),,,,,, [ 6 ],, ( 1) PAN MS,

3 , Gram2 schm idt 1 Fig11 The flowchart of wavelet fusion,, MS 3 (2) SF IM SF IM Smoothing Filter2based Inten2 sity Modulation (L iu 2000) [ 7 ], : DN ( ) fusion = DN ( ) low DN ( ) high DN ( ) mean (1),, ; DN ( ) fusion DN ; DN ( ) low DN ; DN ( ) high DN ; DN ( ) mean,, DN ( ) high DN ( ) mean, SF IM,, 312 (1) Gram2schm idt Gram2schm idt [ 8 ] n n ( 2, 3 ),, Gram2schm idt :, Gram 2schm idt,, Gram2schm idt, Gram2schm idt, Gram 2schm idt PCA PCA ; Gram2schm idt,,, (2) PC I 910 PANSHARP Geomatica Advanced Pan Sharpening Geomati2 ca 9 Prime, [ 9 ] Pansharp,, Pan MS,

4 4 : QuickB ird, QuickB ird, ( 450nm 520nm ) (520nm 660nm) (630nm 690nm ) (760nm 900nm ), 2144m;, 450nm 900nm, 0161m bit Quickbird, ,, RM SE, n, n ; B mean i i ; Bmean i i 4, 4,, Gram2 schm idt Pansharp,, ;, SF IM W avelet SF IM, [ 7 ], 4, ( 412 [ 10 ],,,,, [ 11 ] Pan : : D IF i = 1 n B ijk - B ijk (2), D IF i, i ; j, k ; n ; Bijk ; b ijk : RM S E = n i =1 j k (B mean i - B mean i ) 2 n (3) 4 (RGB: 3, 2, 1) Fig14 O riginal image and fused image (RGB: 3, 2, 1)

5 ),, DN ; ( 6), PC I Pansharp, (1) D IF RMSE, D IF RMSE, 3, Gram2schm idt RMSE, 01414, ; Pansharp, RMSE ; W avelet, RMSE

6 4 : ; SF IM, RMSE, RMSE , QuickB ird 11bit, 16bits unsigned int, ,,, RMSE SF IM, 3 Tab13 The d ifference between fused image and or igna l m ultispectra l image D IF1 D IF2 D IF3 D IF4 RMSE W avelet SF IM Gram_schm idt Pansharp (2) Pan, 4, Pan, 01591, Pan, Gram2schm idt 01930; Pan2 sharp, 01901; SF IM, 01899; Pan Tab14 Correla tion coeff ic ien ts of or ig ina l and fused images w ith Pan image b1 b2 b3 b W avelet SF IM Gram_schm idt Pansharp : (1) W avelet SF IM Gram2schm idt PC I pansharp4 (2), Gram2schm idt, PC I pansharp :,, QuickB ird [ 1 ] [ ].,.. :, [ 2 ].., 2003, (10), [ 3 ] Zhang Y. Understanding image fusion. Photogrametric En2 gineering & Remote Sensing, 2004, 70 (6) : [ 4 ]. Landsat TM /ETM +. ( ), 2007, 32 ( 1), [ 5 ],,.., [ 6 ] Krista Amolins, Yun Zhang, Peter Dare. W avelet based image fusion techniques2an introduction, review and com2 parison. Photogrametry&Remote sensing, 2007, 62: [ 7 ] L IU J G. Smoothing filter2based intensity modulation: a spectral p reserve image fusion technique for imp roving spa2 tial details. Internationl Journal of Remote Sensing, 2000, 21 (18) : [ 8 ] Clayton D G. Gram2Schm idt orthogonalization. App lied Statistics, 1971, 20 (3) : [ 9 ] www1pcigeomatics1com [ 10 ].., 2004, 11 (9), [ 11 ]. Landsat 7 ETM +., 2005, 9 (2) : ,

7 Spectra l F idelity in H igh2resolution Rem ote Sen sing Image Fusion L I Chunhua 1, 2, XU Hanqiu 1 ( 1 College of Environm ent and resources, Fuzhou U niversity, Fuzhou 35000, China; 2 College of Geography, Fujian N orm al U niversity, Fuzhou , China) Abstract: An effective image fusion technique can not only imp rove spatial details but also p reserve the spectral information. M any research papers have reported the lim itations of exiting fusion techniques. The most significant p roblem is spectral distortion. For IHS fusion, a common strategy is to m atch the Pan to the I band before the re2 p lacement, stretch the H and S bands before reversing H IS transform, or stretch individual I, H or S band with re2 spect to individual data sets. In PCA fusion, the solutions are stretching the p rincipal components to give a spheri2 cal distribution, or discarding the first p rincipal component. B y selecting these p roper fusion techniques, success2 ful results can be achieved for the fusion of SPOT Pan w ith M S im ages. However, these techniques are not efficient for the new high2resolution satellite images, such as IKONOS and QuickB ird im ages. This study analyses the rea2 sons for spectral distortion in the new high2resolution image fusion. The research reveals two major reasons. One major reason for the distortion is the wavelength extension of the new satellite Pan image. The radiometric differ2 ence between different sensors is also an important reason if the fusion images are from different sensor system s. Four image fusion algorithm s have been emp loyed to resolve the spectral distortion p roblem in the study of Quick2 B ird im age fusion. These are the filter2based algorithm s such asw avelet and SF IM transform s, and statistics2based algorithm s such as Gram 2schm idit and PC I pansharp modules. The experimental results show that although all these four algorithm s have good spectral fidelity p roperty, the statistics2based algorithm s are generally more efficient than filter2based algorithm s through the quantitative statistical analysis of spectral fidelity and the ability of gaining high frequency information. B ut the research result is only for QB im age. Key words: high2resolution remote sensing image; spectral fidelity; QuickB ird im age