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Not all images are the same. Frequently, to do science you might want to compare two different images at the same resolution, or map them to the same grid, different from the original. To map to lower resolution you should co-add pixels, but to map at a higher resolution you might need to interpolate. This article discusses four interpolations schemes, how to use them, and which might be best for you.

Example of 4 different interpolation styles as applied to mapping from a Cassini/VIMS Titan cube.

Rotating Titan animated .GIF.

Oftentimes when faced with a data-rich environment, a good way to begin the process of analyzing and organizing the data in order to get a look at the big picture is to use a classification scheme. Here I describe some ways to classify data, practical uses, an in-progress application of the data to Visual and Infrared Mapping Spectrometer (VIMS) spectra of Titan, and some links to other places to obtain further information.

I describe here principal components analysis, a method for condensing the information present in images with many colors into fewer channels. This section is heavy on linear algebra—just a warning.

I am presently using this to try to map Titan into spectral classification units using the Visual and Infrared Mapping Spectrometer (VIMS). VIMS takes simultaneous 64×64 images in 256 different infrared channels at wavelengths between 0.9 and 5.2 microns. However, VIMS can only see through Titan's atmosphere and down to the surface in a handful of spectral windows, totalling maybe 20–30 channels. The remaining channels probe different levels in the atmospheric haze, but most are redundant.

I am using principal components analysis (PCA) to bring out subtle variations by reprojecting the VIMS 256-color maps into a different set of orthonormal basis vectors that span the same space, but have most of the data's information in only 9 or 10 channels instead of 256. Woah.