Unmixing is an ill-posed inverse problem and as such the solution computed with different unmixing algorithms depends
on the underlying assumptions for the inverse problem. Ideally one would expect similar solutions for unmixing a
hyperspectral image of different spatial resolutions of the same scene. In this paper, we study the results of unmixing
different images of the same area at different spatial resolution using different unmixing algorithms. We also compare
the estimation of the number of endmembers using the rank of a scaled correlation matrix against the positive rank
estimated with the fitting error of a positive matrix factorization. The results show that algorithms that assume the pure
pixels in the image given consistent results in the same scale and are limited to the number of endmembers determined
from the rank of the scaled correlation matrix while algorithms that do not assume pure pixels are consistent across
spatial scales and the number of endmembers is better estimated by the positive rank. One and four meter data collected
with the AISA sensor over southwestern Puerto Rico is used for the study.
In this paper, we present an experimental comparison of unmixing using the constrained positive matrix
factorization (cPMF) with SMACC and MaxD unmixing algorithms that retrieve endmembers from the image pixels.
The comparison was made using hyperspectral images collected over Vieques Island in Puerto Rico using the AISA
sensor. Based on field work, six information classes were identified in the area of interest and the algorithms are
evaluated in their capability to retrieve information about the classes of interest. The cPMF was the only approach
capable of identifying all six informational classes with one or more spectral classes assigned to them. SMACC and
MaxD were unable to extract one of the classes. The abundance maps from cPMF describe the spatial distribution of the
information classes.
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