Adaptive intensity matching filters: a new tool for multi-resolution data fusion

Many different multiresolution fusion methods have been proposed in the literature. An important actual aim in this field of research is to produce colour composites of multiresolution data preserving both the essential spatial information of the high resolution image and the spectral information content of the low resolution channels, so as to produce pseudo high resolution spectral channels which can be further processed for improved classification or other information extraction purposes. The best integration results in this regard have been obtained by the HPF algorithm and by a new fusion method based on multiresolution analysis of the images using the wavelet transform. A new methodology based on adaptive intensity matching filters using local image statistics to spectrally adjust high resolution images to the radiometry of low resolution channels is described in this paper. The algorithm tends to equalise the mean (LMM algorithm) or the mean and the variance (LMVM algorithm) of the high resolution image with those of the low resolution channels, on a pixel by pixel basis, from the values measured within a local window around each pixel position. The INR (Intensity Normalised Ratio) transform, as defined in this paper, is a fast alternative to the RGB-IHS-RGB transform, and allows an efficient implementation of the intensity matching fusion method, generalised to images with more than three channels. These algorithms are applied to a 1024 x 1024 window extract of a high resolution (5m) KOSMOS KVR 1000 panchromatic image to be fused with a registered low resolution (20 m) SPOT XS image, obtained over the city of Liege (Belgium). The results obtained for varying filtering window sizes are compared with the HPF filter and the wavelet transform applied to the same set of images.