Joint Quality Measure for Evaluation of Pansharpening Accuracy

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, which consists of Means, Standard deviations and Correlation coefficient (CMSC), and is translation invariant with respect to means and standard deviations. The JQM itself consists of a weighted sum of two terms. The first term is measured between a low pass filtered pansharpened image and original multispectral image at a reduced/low resolution scale. The second term is measured between the intensity calculated from spectrally weighted pansharpened multispectral 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 or unexpected properties of the already known measure, Quality with No Reference (QNR), on the other hand.

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

[2]  Luciano Alparone,et al.  MTF-tailored Multiscale Fusion of High-resolution MS and Pan Imagery , 2006 .

[3]  Jocelyn Chanussot,et al.  Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Morton J. Canty,et al.  Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition , 2014 .

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

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

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

[8]  Jocelyn Chanussot,et al.  Pansharpening Quality Assessment Using the Modulation Transfer Functions of Instruments , 2009, IEEE Transactions on Geoscience and Remote Sensing.

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

[10]  Farhad Samadzadegan,et al.  Spatial Quality Assessment of Pan-Sharpened High Resolution Satellite Imagery Based on an Automatically Estimated Edge Based Metric , 2013, Remote. Sens..

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

[12]  G. A. Boggione,et al.  Simulation of a Panchromatic Band by Spectral Combination of Multispectral ETM + Bands , 2003 .

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

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

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

[16]  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..

[17]  Xiang Zhu,et al.  Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content , 2010, IEEE Transactions on Image Processing.

[18]  Jocelyn Chanussot,et al.  Comparison of Pansharpening Algorithms: Outcome of the 2006 GRS-S Data-Fusion Contest , 2007, IEEE Transactions on Geoscience and Remote Sensing.

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

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

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

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

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

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

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

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

[27]  John van Genderen,et al.  Structuring contemporary remote sensing image fusion , 2015 .