EM-based Bayesian Fusion of Hyperspectral and Multispectral images

During the last two decades, the number of spectral bands in optical remote sensing technology kept growing steadily going from multispectral (MS) to hyperspectral (HS) data sets. HS images employ hundreds of contiguous spectral bands to capture and process spectral information over a range of wavelenghts, compared to the tens of discrete spectral bands used in MS images (Chang, 2003). This increase in spectral accuracy is delivering more information, allowing a whole range of new and more precise applications. The detailed spectral information of HS images is helpful for interpretation, classification and recognition. However, in remote sensors, usually a trade-off exists between SNR, spatial and spectral resolutions due to physical limitations, data-transfer requirements and some other practical reasons. In most cases, high spatial and spectral resolutions are not available in a single image, which makes the spatial resolution of HS images usually lower than that of MS images (Gomez et al., 2001). In practice, many applications require high accuracy both spectrally and spatially, which inspires research on spatial resolution enhancement techniques for HS image (Gomez et al., 2001; Duijster et al., 2009; Zhang & He, 2007; Hardie et al., 2004; Eismann & Hardie, 2005; 2004). When more than one observation of the scene is available, a popular technique dealing with this limitation is image fusion, a well studied field for more than ten years. As a prototype problem, usually an image of high spectral resolution is combined with an image of high spatial resolution to obtain an image of optimal resolutions both spectrally and spatially. Most fusion techniques for spatial resolution improvement were developed for the specific purpose of enhancing MS image by using a panchromatic (Pan) image of higher spatial resolution, also referred to as pansharpening. Principal component analysis (PCA) (Chavez et al., 1991; Shettigara, 1992) and Intensity-Hue-Saturation (IHS) transform (Carper et al., 1990; Edwards & Davis, 1994; Tu et al., 2001) based techniques are the most commonly used ones. The Pan image is applied to totally or partially substitute the 1st principal component or intensity component of the coregistered and resampled MS image. To generalize to more than three bands and to reduce spectral degradation, generalized IHS (GIHS) transforms (Tu et al., 2004) and generalized intensity modulation techniques (Alparone et al., 2004) were defined. High-pass filtering and high-pass modulation techniques were developed (Chavez et al., 1991; Shettigara, 1992; Liu &Moore, 1998), in which spatial high-frequency information is extracted and injected adequately into each band of the MS image. With the rise of multiresolution analysis, many researchers have proposed pansharpening techniques, using Gaussian and Laplacian pyramids as well as discrete decimated and undecimated wavelet transforms (WTs) 6