Comparison of four different fusion techniques for IKONOS data

Abstract

Since the advent of high spatial resolution satellite images, the fusion of multiresolution images has been an important field of research. Many methods such as Principal Component Analysis (PCA), Multiplicative Transform, Brovey Transform, IHS Transform and wavelet transform have been developed in the last few years producing good quality fused images. These images are usually characterized by high information content but with significantly altered spectral information content. More recently Yun Zhang has presented a new algorithm for the fusion of Landsat ETM and Ikonos data respectively. In this study we compare the efficiency of four of the above fusion techniques and more especially the efficiency of Modified IHS PCA, Pansharp and Wavelet fusion techniques for the fusion of Ikonos data. The area of interest is situated in Crete, Greece. It is a coastal area. An Ikonos cloud free subscene was used in this comparative study. The nearest neighborhood method has been used for the resampling and the fused images have a 1 meter pixel size. For each merged image we have examined: a) the optical qualitative result; b) the statistical parameters of the histograms of the various frequency bands, especially the standard deviation. All the fusion techniques improve the resolution and the optical result. The Pansharp and the Wavelet merging technique do not change at all the statistical parameters of the original images. The Pansharp merging technique is proposed if the researcher wants to proceed to further processing using for example vegetation indexes or to perform classification using the spectral signatures.