Full text: Papers accepted on the basis of peer-reviewed abstracts (Part B)

In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
i 
387 
Zhang, Y., and Wang, R., 2004. Multi-resolution and multi- 
spectral image fusion for urban object extraction. In: XXth 
ISPRS Congress, Commission III, Istanbul, Turkey, pp. 960- 
966. 
Zhou, J., Civco, D., and Silander, J. 1998. A wavelet transform 
method to merge Landsat TM and SPOT panchromatic data, 
International Journal of Remote Sensing. 19(4), pp. 743-757. 
Spectral consistency 
Spatial consistency 
Fusion 
SSIM, 
SSIM 
ERGAS, 
SAM, 
SSIM 
SSIM 
ERGAS 
CORR 
CORR 
HPCC, 
HPCC 
PC 
PC ZNCC 
Method 
ideal=l 
(mean) 
ideal=0 
ideal=0 
PAN, 
PAN 
PAN, 
PAN, 
PAN 
ideal=l 
(mean) 
ZNCC, 
(mean) 
ideal=l 
(mean) 
ideal=0 
ideal=l 
(mean) 
ideal=l 
1 
ATWT 
0.9527 
0.8883 
1.2804 
1.0164 
0.6339 
0.7488 
3.7802 
0.7939 
0.8412 
0.7604 
0.77 
0.7675 
0.7821 
0.8940 
0.7474 
0.8467 
0.7679 
0.7789 
0.8604 
0.8018 
0.8625 
0.7685 
0.7738 
0.8459 
0.8122 
0.8615 
0.7991 
0.8084 
2 
IHS 
0.1737 
0.2182 
13.0793 
5.2042 
0.6184 
0.6314 
11.1713 
0.9890 
0.9898 
0.9864 
0.99 
0.9566 
0.9589 
0.2042 
0.5890 
0.9930 
0.9882 
0.9630 
0.2767 
0.6870 
0.9876 
0.9860 
0.9571 
3 
PCA 
0.8036 
0.7047 
2.4393 
1.5413 
0.8379 
0.9307 
3.0968 
0.9276 
0.9433 
0.9914 
0.9944 
0.9430 
0.9468 
0.6736 
0.9623 
0.9762 
0.9971 
0.9630 
0.6311 
0.9879 
0.9825 
0.9979 
0.9651 
0.7103 
0.9346 
0.8870 
0.9912 
0.9162 
4 
GIF-1 
0.7462 
0.6405 
2.9900 
1.1484 
0.9040 
0.9457 
3.0098 
0.9349 
0.9545 
0.9929 
0.99 
0.9444 
0.9447 
0.6079 
0.9705 
0.9665 
0.9941 
0.9507 
0.5693 
0.9516 
0.9725 
0.9941 
0.9499 
0.6386 
0.9567 
0.9443 
0.9918 
0.9341 
5 
GIF-2 
0.7057 
0.7076 
2.3506 
0.7142 
0.8947 
0.9359 
3.1691 
0.8960 
0.9257 
0.9846 
0.9885 
0.9157 
0.9216 
(90%) 
0.6666 
0.9628 
0.9571 
0.9928 
0.9494 
0.7293 
0.9551 
0.9520 
0.9912 
0.9233 
0.7288 
0.9308 
0.8978 
0.9854 
0.8980 
Table 1. Spectral and spatial consistency assessment of the pan-sharpened image dataset (first assessment setup) 
Spectral consistency 
Spatial consistency 
Fusion 
SSIM, 
SSIM 
ERGAS, 
SAM, 
SSIM 
SSIM 
ERGAS 
CORR 
CORR 
HPCC, 
HPCC 
PC 
PC ZNCC 
Method 
ideal=l 
(mean) 
ideal=0 
ideal=0 
PAN, 
PAN 
PAN, 
PAN, 
PAN 
ideal=l 
(mean) 
ZNCC, 
(mean) 
ideal=l 
(mean) 
ideal=0 
ideal=T 
(mean) 
ideal=l 
1 
GIF-2 
0.7057 
0.7076 
2.3506 
0.7142 
0.8947 
0.9359 
3.1691 
0.8960 
0.9257 
0.9846 
0.9885 
0.9157 
0.9216 
(90%) 
0.6666 
0.9628 
0.9571 
0.9928 
0.9494 
0.7293 
0.9551 
0.9520 
0.9912 
0.9233 
0.7288 
0.9308 
0.8978 
0.9854 
0.8980 
2 
GIF-2 
0.7333 
0.7366 
2.0316 
0.7002 
0.8591 
0.9091 
3.2778 
0.8827 
0.9136 
0.9816 
0.9837 
0.8116 
0.8131 
(75%) 
0.7011 
0.9335 
0.9389 
0.9874 
0.8654 
0.7594 
0.9300 
0.9350 
0.9852 
0.8097 
0.7529 
0.9139 
0.8980 
0.9808 
0.7658 
3 
GIF-2 
0.8205 
0.8277 
1.4732 
0.6344 
0.7706 
0.8207 
3.5938 
0.8521 
0.8758 
0.9412 
0.9421 
0.7024 
0.6618 
(50%) 
0.8053 
0.8412 
0.8952 
0.9464 
0.6968 
0.8462 
0.8408 
0.8923 
0.9431 
0.6368 
0.8391 
0.8302 
0.8638 
0.9379 
0.6114 
Table 2. Spectral and spatial consistency assessment of GIF-2 pan-sharpened image dataset (second assessment setup) 
Added high frequency, % (GIF-2 method) 
Figure 2. Dependency of spectral consistency measures on 
added high frequency in GIF-2 method (Table 2) 
Figure 3. Dependency of spatial consistency measures on 
added high frequency in GIF-2 method (Table 2)
	        
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