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http://www.columbia.edu/itc/applied/e3101/SVD_applications.pdf

I need help on understanding the second part of the document, page 13 onwards. On page 15, it showed 3 data sets, relative elevation as a function of kilometers across axis, however at page 16, the author constructed a matrix ##A## which is ##179 \times 80##. This is where I get lost. How did it come up with such a matrix of such size? Is the 80 here the number of x-axis points on the data set (assuming each point has a frequency of 1 kilometers across axis)? How about the 179? I'm supposed to do something similar, but instead of ocean ridges I need to apply it with spectral reflectances.

I have a basic understanding of what is Singular Value Decomposition (SVD), but I am not completely familiar with it. For example, I do not know how to acquire the eigenvalues acquired from SVD, since I will most likely be using the Matlab built-in function to calculate the SVD of a matrix.

Thank you in advance.