SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES

Spatial quality and spectral quality are the two important indexes that are used to evaluate the quality of any fused image. Most of the existing methods for this purpose, however, considered only the spectral quality except for that proposed by Zhou et al., while the latter cannot be used to compare directly the spatial resolution between the fused image and original high-resolution. A new spatial quality assessment approach was presented in the paper based on the blur parameter estimation. It is based fact that the blur parameter, which is a measure of the spread of the sensor’s point spread function (PSF), characterizes the spatial resolution of the sensor image. The merged images derived from IHS, PCA, HPF, and AWL methods are compared in order to evaluate the performance of the proposed spatial quality measure. Experimental results demonstrate that the spatial quality measure based on the blur parameter is more efficient in judging spatial quality of the fused image. The blur parameter is an objective quality measure based on a basic optical mechanism and thus can be used to evaluate the effectiveness of various processing or fusion schemes in terms of spatial quality of the fused image. It is also shown that the spatial resolution of the fused images can not completely match with the same spatial resolution of the high-resolution panchromatic image. The proposed approach is based upon simple concepts, easy to understand and easy to implement and use.

[1]  Y. Zhang,et al.  A new merging method and its spectral and spatial effects , 1999 .

[2]  Xavier Otazu,et al.  Multiresolution-based image fusion with additive wavelet decomposition , 1999, IEEE Trans. Geosci. Remote. Sens..

[3]  Jun Li,et al.  PCA and wavelet transform for fusing panchromatic and multispectral images , 1999, Defense, Security, and Sensing.

[4]  Thierry Ranchin,et al.  The ARSIS method: a general solution for improving spatial resolution of images by the means of sensor fusion , 1996 .

[5]  E. LeDrew,et al.  Application of principal components analysis to change detection , 1987 .

[6]  Gopal Surya,et al.  Three-dimensional scene recovery from image defocus , 1994 .

[7]  Jan Flusser,et al.  A moment-based approach to registration of images with affine geometric distortion , 1994, IEEE Trans. Geosci. Remote. Sens..

[8]  W. J. Carper,et al.  The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data , 1990 .

[9]  J. Schott,et al.  Resolution enhancement of multispectral image data to improve classification accuracy , 1993 .

[10]  J. Zhou,et al.  A wavelet transform method to merge Landsat TM and SPOT panchromatic data , 1998 .

[11]  B. S. Manjunath,et al.  Multi-sensor image fusion using the wavelet transform , 1994, Proceedings of 1st International Conference on Image Processing.

[12]  P. S. Chavez,et al.  Comparison of the spectral information content of Landsat Thematic Mapper and SPOT for three different sites in the Phoenix, Arizona region , 1988 .