Evolving Detectors for Locating Methane Plumes in Hyperspectral Images

Identifying methane plumes in Earth observation image data is vital in environmental monitoring. This process is the key to locating methane emissions, which is critical for efforts aimed at reducing their contribution to climate change. However, the potential overlaps between methane and other gases in their spectral features make the identification of methane emitters challenging. Therefore, new, more effective and efficient algorithms are needed to address this issue. We propose incorporating methane enhancement maps extracted from image data using improved matched filtering into the feature vectors that undergo evolutionary selection before feeding them to supervised classification models. Therefore, to tackle the problem of methane detection and localization in hyperspectral images, we use a framework that combines evolutionary techniques for feature selection, followed by supervised machine learning for classifying hyperspectral superpixels as those containing or not containing methane. The experiments over the STARCOP benchmark dataset show that even if we exploit no more than three features, we obtain models whose quality, quantified as the area under the receiver operating curve, reaches 0.885 (with a sensitivity of 0.880).