Comparing distances for quality assessment of fused images

This communication deals with the fusion of panchromatic (PAN) images of high spatial resolution and multispectral (MS) images of lower resolution in order to synthesize MS images at high resolution. These fused images should be as identical as possible to images that would have been acquired by the corresponding space borne sensor if it were fit with this high resolution. A protocol for the assessment of the quality of the fused images was discussed by the EARSeL Special Interest Group ‘‘data fusion'' in 2004. It evaluates how much fused images comply with two properties, on multispectral and monospectral viewpoints. The compliance is measured through a set of distances between the set of fused images and the multispectral reference images. This communication analyses the distances that are found in literature. First of all, it proposes a classification of these distances into seven categories. Then it shows some relations between several distances through an empirical study. Finally, a typical choice of distances is proposed in order to assess most aspects of fused images.

[1]  Yufeng Zheng,et al.  A new metric based on extended spatial frequency and its application to DWT based fusion algorithms , 2007, Inf. Fusion.

[2]  Yaonan Wang,et al.  Combination of images with diverse focuses using the spatial frequency , 2001, Inf. Fusion.

[3]  Lucien Wald,et al.  Analysis of Changes in Quality Assessment with Scale , 2006, 2006 9th International Conference on Information Fusion.

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

[5]  Lucien Wald,et al.  Data Fusion. Definitions and Architectures - Fusion of Images of Different Spatial Resolutions , 2002 .

[6]  Henk J. A. M. Heijmans,et al.  A new quality metric for image fusion , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[7]  Paul S. Fisher,et al.  Image quality measures and their performance , 1995, IEEE Trans. Commun..

[8]  Xavier Otazu,et al.  Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Luciano Alparone,et al.  A global quality measurement of pan-sharpened multispectral imagery , 2004, IEEE Geoscience and Remote Sensing Letters.

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

[11]  Lucien Wald,et al.  A MTF-Based Distance for the Assessment of Geometrical Quality of Fused Products , 2006, 2006 9th International Conference on Information Fusion.

[12]  Sophia Antipolis,et al.  Assessment of the quality of fused products , 2004 .

[13]  Jun Li.  SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2000 .

[14]  Luciano Alparone,et al.  Remote sensing image fusion using the curvelet transform , 2007, Inf. Fusion.

[15]  Mario Lillo-Saavedra,et al.  Fusion of multispectral and panchromatic satellite sensor imagery based on tailored filtering in the Fourier domain , 2005 .

[16]  L. Wald,et al.  Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images , 1997 .

[17]  Thierry Ranchin,et al.  A modular platform for fusion of images , 2005 .

[18]  Laura Igual,et al.  A Variational Model for P+XS Image Fusion , 2006, International Journal of Computer Vision.

[19]  L. Wald,et al.  Fusion of high spatial and spectral resolution images : The ARSIS concept and its implementation , 2000 .

[20]  Thierry Ranchin,et al.  Evaluation of the quality of Quickbird fused products , 2004 .

[21]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.