Title :
Hyperspectral pansharpening using QNR optimization constraint
Author :
Khan, Muhammad Murtaza ; Chanussot, Jocelyn ; Alparone, Luciano
Author_Institution :
Dept. of Images & Signals, Grenoble Inst. of Technol., Grenoble, France
Abstract :
This paper presents a method for pansharpening of low resolution Hyperspectral (HS) images. The proposed method is based upon the optimization of both the spectral and spatial quality criteria of the QNR quality assessment index. The simultaneous optimization of the spectral and spatial quality constraints is obtained by means of the Pareto solutions, obtained by making use of an evolutionary algorithm. A selection criteria is defined to select a single solution from among the Pareto solutions and the results obtained show both quantitative and qualitative improvement over the results obtained by some existing pansharpening methods.
Keywords :
Pareto optimisation; evolutionary computation; geophysical signal processing; image resolution; Pareto solution; QNR optimization constraint; QNR quality assessment index; evolutionary algorithm; hyperspectral image resolution pansharpening; selection criteria; spatial quality criteria; Constraint optimization; Frequency; Hyperspectral imaging; Image resolution; Layout; Optical distortion; Pareto optimization; Quality assessment; Signal resolution; Spatial resolution; Fusion; Hyperspectral; Pansharpening; Quality Not-requiring Reference (QNR) index; Universal Image Quality Index (UIQI);
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4686-5
Electronic_ISBN :
978-1-4244-4687-2
DOI :
10.1109/WHISPERS.2009.5289027
