We focus on the problem of spatial feature correspondence between images generated by sensors operating in different regions of the spectrum, in particular the Visible (Vis: 0.4-0.7 μm) and Shortwave Infrared (SWIR: 1.0-2.5 μm). Under the assumption that only one of the available datasets is geospatial ortho-rectified (e.g., Vis), this spatial correspondence can play a major role in enabling a machine to automatically register SWIR and Vis images, representing the same swath, as the first step toward achieving a full geospatial ortho-rectification of, in this case, the SWIR dataset. Assuming further that the Vis images are associated with a Lidar derived Digital Elevation Model (DEM), corresponding local spatial features between SWIR and Vis images can also lead to the association of all of the additional data available in these sets, to include SWIR hyperspectral and elevation data. Such a data association may also be interpreted as data fusion from these two sensing modalities: hyperspectral and Lidar. We show that, using the Scale Invariant Feature Transformation (SIFT) and Optimal Randomized RANdom Sample Consensus (RANSAC) algorithm, a software method can successfully find spatial correspondence between SWIR and Vis images for a complete pixel by pixel alignment. Our method is validated through an experiment using a large SWIR hyperspectral data cube, representing a portion of Los Angeles, California, and a DEM with associated Vis images covering a significantly wider area of Los Angeles.