Abstract: Recent
developments in
machine learning are
applied to in-situ
data collected by a
Multi-Angle
Snowflake Camera
(MASC),
incorporating
convolutional and
residual networks in
big data
environments. These
networks provide the
following benefits:
require little initial
preparation and
automatic feature
extraction, high
accuracy and
through transfer
learning techniques,
and relatively small
training sets. The
networks have large
supporting
communities and are
popular for image
processing and
classification tasks
specifically. In this
paper, a
convolutional neural
network (CNN) is
adapted and tasked
with classifying
images captured
from two storm
events in December
2014 and February
2015 in Greeley,
Colorado. A training
data set containing
1400 MASC images
was developed by
visual inspection of
recognizable
snowflake
geometries and
sorted into six
distinct classes. The
network trained on
this data set
achieved a mean
accuracy of 93.4%
and displayed
excellent generality.
A separate training
data set was
developed sorting
flakes into three
classes showcasing
distinct degrees of
riming. The network
was then tasked with
classifying images
and estimating
where flakes fell
within this riming
scale. The riming
degree estimator
yields promising
initial results but
would benefit from
larger training sets.
Future applications
are discussed.
Adviser: Branislav Notaros Co-Adviser: N/A Non-ECE Member: Christine Chiu (Atmospheric Science) Member 3: Ali Pezeshki (Electrical and Computer Engineering) Addional Members: N/A
Publications: None
Program of Study: ECE506 ECE421 ECE512 ECE521 ECE540 ECE580B5 ECE656 ECE699
