Mars, Jupiter, Saturn, Titan

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qview measure

I am using ISIS2 and qview to look at processed MOC cubes. When I try to use the qview measure tool, one end of the measuring line is always trapped in the upper left hand corner. Has anyone else experienced this and/or found a way to fix this?

MGS data tools

Not quite sure where to start with data from the Mars Global Surveyor spacecraft? This article contains links to the raw data, and also links to the various software tools that members of the community have created to analyze the data itself.

MOC images vs press releases

How do I get science-quality MOC images from a press release image I like? For example, I want to use this press release image:

http://www.msss.com/mars_images/moc/2004/04/30/

The numbering systems seem totally different though, so I don't know how to find that image in the data at:

http://ida.wr.usgs.gov/orb_page.html

Rotating Titan

Rotating Titan

Rotating Titan animated .GIF.

MOLA maps with GMT

Creating shaded relief maps or combination colorized/shaded relief maps with MOLA data is easy to do with the Generic Mapping Tools (GMT).

Importing MOLA data into GMT

The MOLA gridded data set is a great resource. The Generic Mapping Tools (GMT) are a great tool set. This article shows you how to take the raw binary MOLA gridded data set and convert it into the netCDF format that GMT uses. Once you do that, a whole range of mapping options are open to you.

Data classification / cluster analysis

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.

Titan Tb color

Titan Tb color

VIMS true color and principal components

VIMS true color and principal components

It's not just about squiggly lines anymore: extracting information from spectral mapping using principal components analysis

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.

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