Local Background Estimation for Improved Gas Plume Identification in
  Hyperspectral Images
Scout Jarman Zigfried Hampel-Arias Adra Carr Kevin R. Moon
Deep learning identification models have shown promise for identifying gas
plumes in Longwave IR hyperspectral images of urban scenes, particularly when a
large library of gases are being considered. Because many gases have similar
spectral signatures, it is important to properly estimate the signal from a
detected plume. Typically, a scene's global mean spectrum and covariance matrix
are estimated to whiten the plume's signal, which removes the background's
signature from the gas signature. However, urban scenes can have many different
background materials that are spatially and spectrally heterogeneous. This can
lead to poor identification performance when the global background estimate is
not representative of a given local background material. We use image
segmentation, along with an iterative background estimation algorithm, to
create local estimates for the various background materials that reside
underneath a gas plume. Our method outperforms global background estimation on
a set of simulated and real gas plumes. This method shows promise in increasing
deep learning identification confidence, while being simple and easy to tune
when considering diverse plumes.