From UNB PanSharp to Fuze Go – the success behind the pan-sharpening algorithm

The pan-sharpening algorithm, known as UNB PanSharp, was adopted by PCI Geomatics in 2002 and by DigitalGlobe (DG) in 2003, resulting in PCI PanSharp and DG PanSharp. Now, UNB PanSharp is developed as a stand-alone software tool, named Fuze Go™, that can be used stand-alone, integrated into ENVI and potentially into other major geo-spatial software packages such as EDARS, ESRI, and SOCET. In addition, it is in the process to integrate Fuze Go into GeoMarketSpace – a new online system that turns all geospatial information into answers. What has made UNB PanSharp successful for the last 10 years and still standing out from millions of research publications in the area? This article will go through the general principles of individual pan-sharpening algorithms that have been adopted by industry and widely used by users globally to find out the differences between UNB PanSharp and the other pan-sharpening algorithms.

[1]  Manfred Ehlers,et al.  Performance of evaluation methods in image fusion , 2009, 2009 12th International Conference on Information Fusion.

[2]  D. Holcomb,et al.  Optimizing the High-Pass Filter Addition Technique for Image Fusion , 2007 .

[3]  Yun Zhang,et al.  METHODS FOR IMAGE FUSION QUALITY ASSESSMENT - A REVIEW, COMPARISON AND ANALYSIS , 2008 .

[4]  S. Ashraf,et al.  Image data fusion for the remote sensing of freshwater environments , 2012 .

[5]  George P. Lemeshewsky Multispectral multisensor image fusion using wavelet transforms , 1999, Defense, Security, and Sensing.

[6]  Yun Zhang,et al.  A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[7]  Roger L. King,et al.  A wavelet based algorithm for pan sharpening Landsat 7 imagery , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[8]  Jun Li SPATIAL QUALITY EVALUATION OF FUSION OF DIFFERENT RESOLUTION IMAGES , 2010 .

[9]  George P. Lemeshewsky,et al.  Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges , 2002, SPIE Defense + Commercial Sensing.

[10]  K. Nikolakopoulos Comparison of Nine Fusion Techniques for Very High Resolution Data , 2008 .

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

[12]  Yun Zhang,et al.  Understanding image fusion , 2004 .

[13]  James E. McMurtrey,et al.  Demonstration of the accuracy of improved-resolution hyperspectral imagery , 2002, SPIE Defense + Commercial Sensing.

[14]  S. Sides,et al.  Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic , 1991 .