Color and spatial distortions of pan-sharpening methods in real and synthetic images

Abstract

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.