Joint quality measure for accuracy assessment of pansharpening methods

A new joint quality measure JQM which is a sole measure is proposed for quality ranking of pansharpening methods. It is based on a newly proposed composite similarity measure CMSC which consists of means, standard deviations and correlation coefficient and is translation invariant with respect to all parameters. JQM itself consists of a weighted sum of two terms. First term is measured between a low pass filtered pansharpened image and original multispectral image in a reduced resolution scale. The second one - between weighted intensity calculated from pansharpened image and original panchromatic image in a high resolution scale. Experimental results show advantages of a new measure JQM for quality assessment of pansharpening methods on the one hand and drawbacks of already known measure QNR on the other hand.

[1]  C. Padwick,et al.  WORLDVIEW-2 PAN-SHARPENING , 2010 .

[2]  Gintautas Palubinskas.  Quality assessment of pan-sharpening methods , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[3]  Gintautas Palubinskas.  Mystery behind similarity measures mse and SSIM , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  Peter Reinartz,et al.  Multi-resolution, multi-sensor image fusion: general fusion framework , 2011, 2011 Joint Urban Remote Sensing Event.

[5]  M. Canty.  Image Analysis, Classification, and Change Detection in Remote Sensing , 2006 .

[6]  Gintautas Palubinskas,et al.  Fast, simple, and good pan-sharpening method , 2013 .

[7]  Bruno Aiazzi,et al.  A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images , 2009, IEEE Geoscience and Remote Sensing Letters.

[8]  S. Baronti,et al.  Multispectral and panchromatic data fusion assessment without reference , 2008 .

[9]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[10]  Yun Zhang,et al.  Recent advances in pansharpening and key problems in applications , 2014 .

[11]  Luciano Alparone,et al.  Quality assessment of pansharpening methods and products , 2011 .

[12]  Peter Reinartz,et al.  Selection of numerical measures for pan-sharpening assessment , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Jixian Zhang,et al.  Pansharpening: from a generalised model perspective , 2014 .

[14]  Peter Reinartz,et al.  Analysis and selection of pan-sharpening assessment measures , 2012 .

[15]  Andrea Garzelli,et al.  Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis , 2002, IEEE Trans. Geosci. Remote. Sens..

[16]  Leszek Wojnar,et al.  Image Analysis , 1998 .

[17]  Qingquan Li,et al.  A comparative analysis of image fusion methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[18]  S. Klonus,et al.  Image Fusion Using the Ehlers Spectral Characteristics Preservation Algorithm , 2007 .