Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery

Satish Kumar, Carlos Torres, oytun ulutan, alana ayasse, Dar Roberts, B.S. Manjunath

Abstract:

Effective analysis of hyperspectral imagery is essential
for gathering fast and actionable information of large areas
affected by atmospheric and green house gases. Existing
methods, which process hyperspectral data to detect amorphous gases such as CH4 require manual inspection from
domain experts and annotation of massive datasets. These
methods do not scale well and are prone to human errors
due to the plumes’ small pixel-footprint signature. The proposed Hyperspectral Mask-RCNN (H-mrcnn) uses principled statistics, signal processing, and deep neural networks
to address these limitations. H-mrcnn introduces fast algorithms to analyze large-area hyper-spectral information
and methods to autonomously represent and detect CH4
plumes. H-mrcnn processes information by match-filtering
sliding windows of hyperspectral data across the spectral
bands. This process produces information-rich features that
are both effective plume representations and gas concentration analogs. The optimized matched-filtering stage processes spectral data, which is spatially sampled to train an
ensemble of gas detectors. The ensemble outputs are fused
to estimate a natural and accurate plume mask. Thorough
evaluation demonstrates that H-mrcnn matches the manual
and experience-dependent annotation process of experts by
85% (IOU). H-mrcnn scales to larger datasets, reduces the
manual data processing and labeling time (×12), and produces rapid actionable information about gas plumes