The Analysis of the Effects of Principle Component Analysis on Mars Image Mosaics

Sean David Hoyt


Our overall goal is to measure the water content of clouds in the Martian atmosphere as a function of time over diurnal, seasonal, and interannual scales using ground-based, near-infrared spectral images. To do this, the surface reflectance must be understood well enough so that its spectral signature can be removed. Principle Component Analysis (PCA) is used to reduce the dimensionality of the data in order to recover the smallest number of surface endmember spectra needed to model the surface reflectance. During the 1999 opposition, the angular size of Mars was so large that its projected size was larger than the camera detector. Therefore, the images of Mars were taken in four quadrants. Before PCA could be performed on the data, the images needed to be stitched together. PCA was then done on the mosaic images. The mosaic PCA results are compared to previous results, which show PCA eigenvectors are fairly consistent across all timescales. We present here the results of that task and show that the overall effect on the PCA of the residual mosaic stitch lines is minimal.


Mars; PCA; Mosaics

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