PROCEEDINGS ARTICLE | November 8, 2012
Proc. SPIE. 8537, Image and Signal Processing for Remote Sensing XVIII
KEYWORDS: Image fusion, Wavelet transforms, Principal component analysis, Visualization, Image processing, Wavelets, Multispectral imaging, Image quality, Spatial resolution, Lanthanum
Image fusion is the process of combining information from two or more images into a single composite image that is
more informative for visual perception or additional processing. Pan-sharpening algorithms work either in the spatial or
in the transform domain and the most popular and effective methods include arithmetic combinations (Brovey
transform), the intensity-hue-saturation transform (IHS), principal component analysis (PCA) and different multiresolution
analysis-based methods, typically wavelet transforms. In recent years, a number of image fusion quality
assessment metrics have been proposed. Automatic quality assessment is necessary to evaluate the possible benefits of
fusion, to determine an optimal setting of parameters, as well as to compare results obtained with different algorithms to
check the improvement of spatial resolution while preserving the spectral content of the data. This work addresses the
challenging topic of the quality evaluation of pan-sharpening methods. In particular, a database with a synthetic image
and real GeoEye satellite data was created and several pan-sharpening methods were implemented and tested. Some
interesting results about the color and the spatial distortions of each method were presented and it was demonstrated that
some colors bands are more affected than others depending on the fusion techniques. After the evaluation of these fusion
algorithms, we can conclude that, in general, the à trous wavelet-based methods achieve the best spectral performance
while the IHS-based techniques attain the best spatial accuracy.
