A practical alternative for fusion of hyperspectral data with high resolution imagery

Data fusion techniques are currently used to merge low resolution multispectral with high resolution panchromatic data. Various authors have expended efforts on attempting to achieve a fusion where the spectral characteristics of one image are combined with the spatial attributes of the second producing a new, fused image. This philosophy has a number of inherent problems. The first is that of retaining the spectral fidelity of the original hyperspectral data in the fused data set. Techniques currently used to achieve the fusion usually do not retain the spectral characteristics accurately. Associated with this is the assumption that the intensity of distributions associated with the high spatial resolution (often panchromatic) image, usually collected in the visible portion of the spectrum, are consistent throughout the entire spectral range in question. Another problem is that of image size. To fuse a 20 meter MSS with a 1 meter panchromatic image represents an associated 400 fold increase in data base size. The approach in this extracts the spatial attributes of the high resolution data set (in our case the individual tree crowns). Once extracted, the spatial properties of the extracted attributes are assessed and translated into a spatial coverage consistent with the resolution of the spectral data for further analyses. The advantages of this approach are firstly that the data are used at the resolutions that they were originally collected. Secondly, it avoids the creation of large, unmanageable images.