Mosaicking of global planetary image datasets: 1. Techniques and data processing for Thermal Emission Imaging System (THEMIS) multi-spectral data

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Computational Geophysics: Data Analysis: Algorithms And Implementation, Computational Geophysics: Image Processing, Planetary Sciences: Solid Surface Planets: Remote Sensing, Planetary Sciences: Solid Surface Planets: Surface Materials And Properties, Planetary Sciences: Solid Surface Planets: Instruments And Techniques

Scientific paper

The mosaicking of global planetary data sets allows for the examination of local, regional, and global scale processes on all planetary bodies. Processing techniques that allow us and other users to crate mosaics of tens of thousands of images are documented along with the associated errors introduced by each image-processing algorithm. These techniques (e.g., non-uniformity correction, running contrast stretches, line and row correlated noise removal, and random noise removal) were originally developed for the 2001 Mars Odyssey Thermal Emission Imaging System (THEMIS) infrared multispectral imager data but can be adapted and applied to other data sets by the alteration of input parameters. The techniques for mosaicking planetary image data sets (e.g., image registration, blending, and normalization) are also presented along with the generation of qualitative and quantitative products. These techniques are then applied to generate THEMIS daytime and nighttime infrared, Viking, Context Imager (CTX), and Mars Orbiter Camera (MOC) visible mosaics using a variety of input and output types at a variety of scales. By creating mosaics of the same area using different data sets such as those that illustrate compositional diversity, thermophysical properties, or small-scale morphology, it is possible to view the surface of the planet and geologic problems through many different perspectives. In addition to the techniques used to create large-scale seamless mosaics, we also present the THEMIS daytime and nighttime relative temperature global mosaics, which are the highest resolution (100m/pixel) global scale data sets available for Mars to date.

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