Model Adjusted Matched Filter for Methane Plume Detection on Prisma Hyperspectral Images

Elyes Ouerghi, Thibaud Ehret, Gabriele Facciolo, Enric Meinhardt, Carlo de Franchis, Alexis Groshenry, Jean-Michel Morel

Abstract: Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically point source methane leaks using high resolution hyperspectral images from the PRISMA satellite. We propose an improvement of the classical matched filter method by using an adjustment coefficient. We introduce this new method under the name: Model Adjusted Matched Filter (MAMF). We show that the MAMF method reduces the fraction of false detections compared to the Matched Filter (MF) and the Adaptive Cosine Estimator (ACE) without preventing the detection of plumes. To validate the method, we use a dataset of manually annotated plumes on PRISMA images. We then show that our method outperforms the matched filter and the adaptive cosine estimator in terms of F1 score.