Ensemble segmentation for improved background estimation and gas plume identification in hyperspectral images

Scout Jarman; Tory Carr; Zigfried Hampel-Arias; Eric Flynn; Kevin Moon

doi:10.1117/12.2677729

4 October 2023 Ensemble segmentation for improved background estimation and gas plume identification in hyperspectral images

Scout Jarman, Tory Carr, Zigfried Hampel-Arias, Eric Flynn, Kevin Moon

Author Affiliations +

Proceedings Volume 12675, Applications of Machine Learning 2023; 1267506 (2023) https://doi.org/10.1117/12.2677729
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Abstract

Longwave Infrared hyperspectral images (LWIR HSI) are a powerful data source for various applications in national security and environmental monitoring. A promising area for applying machine learning to LWIR HSI data is for gas plume identification from remote sensing platforms. However, a significant practical difficulty in using HSI for this task is the ability to estimate and remove the background spectra underlying a detected gas plume. Typically, one estimates a covariance matrix and a mean spectrum using all pixels from an image to whiten the pixels of interest before substance identification. We propose using image segmentation to define local regions to perform this whitening. We investigate both local and global estimation of the covariance and mean spectrum, and find that using the global covariance and local mean increases prediction confidence using our deep learning classification model. Using an airborne LWIR capture of the Los Angeles basin, we investigate performance increases by generating an ensemble of random marker-based Watershed segmentations. The ensemble of segmentations provides nuanced mean estimates for each pixel in the gas plume, leading to increased machine learning classification confidence. This method shows significant promise for improving machine learning classification applied to real-world HSI collects.

Conference Presentation

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Scout Jarman, Tory Carr, Zigfried Hampel-Arias, Eric Flynn, and Kevin Moon "Ensemble segmentation for improved background estimation and gas plume identification in hyperspectral images", Proc. SPIE 12675, Applications of Machine Learning 2023, 1267506 (4 October 2023); https://doi.org/10.1117/12.2677729

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