[X] My Assistant

[Nirgal]

Hi all,

since the possiblility of using PCA (Principal Components Analysis) in analyzing multi band
MER pancam data has been mentioned before in the forum (among others by slinted, tedstryk)
I'd like to share the results of my experiments using my suite of C implementations of
various multivariate analysis algorithms applied to multi-band MER pancam data.

(short recap: the goal of this analysis is to reduce the dimensionality of the multi-filter remote sensing
input or feature space (e.g. 6 dimensional for 6-filter frame sequences) in a low dimensional
"representation space", e.g. 1-dimensional for greyscale or 3-dimensional for RGB color images)

In my experiments I'vebeen using a combination of Principal Components Analysis and Self Organizing
Neural Network Feature Maps as follows:



In this example, the neural network was used to project the original six dimensional feature space
of the L234567 sequence to a 2-dimensional neural grid which in turn was used to
project feature points to the LAB color space and finally transformed to RGB space used to false
color the original image on the left hand side.
The right hand side window illustrates the feature space with the white dots representing
PCA projections of samples 6 dimensional input pixels and the blue grid is a projection of the
neural network after a training phase to adapt to the structure of the feature space as close as possible.

In the feature space representation, on can see an obvious grouping of pixels into distinct clusters
possibly each representing different surface compositions.

Remarkably, the "intrinsic dimension" of the six dimensional feature space is just 2, with the most
prominent component of course being the "brightness" channel which confirms the observation that
color variation in the Martian environment in general is not very complex ...

Here are some other examples with L234567 sequences:




Unfortunately, other than a general interest in implementing, experiencing and playing with the
algorithms (and using them for aesthetic purposes in my color image processing) I am by no means
an expert enough to geologically/chemically judge the value and implications of such analysis and I am sure that JPL must have implemented something similar ... would be interesting to know their results