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\begin{document}

\title{Improving Deep Learning Methods~ for Robust Methane Plume Detection
using Alternative Input Representations}



\author[1,2]{Anagha Satish}%
\author[2]{Brian Bue}%
\author[2]{Jake Lee}%
\author[2]{Andrew Thorpe}%
\author[3]{Daniel Cusworth}%
\author[3]{Alana Ayasse}%
\author[3,4]{Riley Duren}%
\affil[1]{California Institute of Technology}%
\affil[2]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[3]{Carbon Mapper, Inc.}%
\affil[4]{University of Arizona}%


\vspace{-1em}



  
  \date{May 18, 2024}


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\begin{abstract}
Methane (CH4) is a prominent greenhouse gas responsible for about 20\%
of all atmospheric radiative forcing. As we notice trends in increasing
global temperatures, understanding and detecting these emissions has
become increasingly important. This requires the creation of robust
greenhouse gas plume detectors. Previous work at the NASA Jet Propulsion
Laboratory has shown Convolutional Neural Networks (CNN) to be an
appropriate solution to map methane sources from future imaging
spectrometer missions, such as Carbon Mapper. However, current models
suffer from a high rate of false positives due to false enhancements in
the detected images.

We have compiled datasets from two Airborne Visible/Infrared Imaging
Spectrometer Next Generation (AVIRIS-NG) California campaigns. We then
trained a GoogleNet CNN Classifier model on each campaign. The baseline
current model uses a Unimodal column-wise matched filter (CMF). This
results in a model known to be sensitive to false enhancements, such as
water/water vapor, bright/dark surfaces, or confuser materials with
similar absorption wavelengths to methane. We first note improvements
between the Unimodal CMF model and a new Surface-Controlled CMF model,
whose dataset matches that of the Unimodal CMF model, but removes
enhancements not matching the absorption wavelength of methane. From
this, we note minimal improvement (1\% increase in F1 score). We then
experiment with various auxiliary products measuring albedo (rgbmu,
SWALB), vegetation (NDVI, ENDVI), and water (h2o, NDWI) indices designed
to combat issues known to produce false enhancements. After training on
these new input representations for both campaigns, we noticed a
significant improvement in the multi-channel model's results. We observe
an increase in the F1 score for classifying positive tiles from 0.78 to
0.86 when trained using auxiliary albedo indices, showing promise for
future use of auxiliary products in improving methane plume detectors.
\textbf{}%
\end{abstract}%



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