Deep Remote Sensing Methods for Methane Detection in Overhead Hyperspectral Imagery

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 CH
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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 CH
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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.