  In the past years, the development of $\mu$LED arrays gained momentum since
they combine the advantages of $\mu$LEDs, such as high brightness and
longevity, with a high resolution of a micro-scaled structure. For the
development, spatially resolved measurements of luminance and color of single
$\mu$LEDs and the entire light-emitting surface are analyzed as they are
crucial for the visual perception. However, the former is time intense in
measurement and evaluation, and the latter suffers from interference caused by
nonfunctional $\mu$LEDs. This paper presents a method to perform both analyzes
with a single measurement using unsupervised machine learning. The results
suggest that a precious reconstruction of the $\mu$LEDs and a more accurate
characterization $\mu$LED arrays can be achieved.
