THEMIS Global Mosaics

We have developed techniques to make seamless, controlled global mosaics from the more than 50,000 multi-spectral infrared images of the Mars returned by the THEMIS instrument aboard the Mars Odyssey spacecraft. These images cover more than 95% of the surface at 100m/pixel resolution at both day and night local times. Uncertainties in the position and pointing of the spacecraft, varying local time, and imaging artifacts make creating well-registered mosaics from these datasets a challenging task. In preparation for making global mosaics, many full-resolution regional mosaics have been made. These mosaics typically cover an area 10x10 degrees or smaller, and are constructed from only a few hundred images. To make regional mosaics, individual images are geo-rectified using the USGS ISIS software. This dead-reckoning is sufficient to approximate position to within 400m in cases where the SPICE information was downlinked. Further coregistration of images is handled in two ways: grayscale differences minimization in overlapping regions through integer pixel shifting, or through automatic tie-point generation using a radial symmetry transformation (RST). The RST identifies points within an image that exhibit 4-way symmetry. Martian craters tend to to be very radially symmetric, and the RST can pin-point a crater center to sub-pixel accuracy in both daytime and nighttime images, independent of lighting, time of day, or seasonal effects. Additionally, the RST works well on visible-light images, and in a 1D application, on MOLA tracks, to provide precision tie-points across multiple data sets. The RST often finds many points of symmetry that aren't related to surface features. These "false-hits" are managed using a clustering algorithm that identifies constellations of points that occur in multiple images, independent of scaling or other affine transformations. This technique is able to make use of data in which the "good" tie-points comprise even less than 1% of total candidate tie-points. Once tie-points have been identified, the individual images are warped into their final shape and position, and then mosaiced and blended. To make seamless mosaics, each image can be level adjusted to normalize its values using histogram-fitting, but in most cases a linear contrast stretch to a fixed standard deviation is sufficient, although it destroys the absolute radiometry of the mosaic. For very large mosaics, using a high-pass/low-pass separation, and blending the two pieces separately before recombining them has also provided positive results.