Spectral Patterns and Reduced-Band Neural Learning in UAV Hyperspectral Images of a Landfill

Diffuse methane escape through landfill covers motivates the search for surface indicators in drone hyperspectral images, but no measurement in this campaign links imaged surfaces to emissions. We therefore ask two technical questions: how matches to an explicit spectral rule are organised across a scene, and how well that rule can be approximated from fewer bands. Ten images of an active landfill (Los Barrios, Spain) were analysed, with earlier gas measurements as context. We grouped 32,238,815 spectral curves by shape without any reference, compared each group’s representative curve with published spectra of hydrocarbon-altered materials, and checked each pixel over a wider range. Two shortwave-infrared groups held 97.7% of the individual matches while covering 53.7% of evaluable observations. This concentration follows from related criteria; it persisted under a second initialisation and stayed above 94% under 21 perturbations of the rule’s thresholds, smoothing and digitisation, although the number and identity of the matches varied. Small neural networks approximated the rule from fewer bands: the multilayer perceptron had the highest observed mean area under the receiver operating characteristic curve, 0.917 over four held-out zones (0.891–0.936), against 0.887 for logistic regression and 0.822 for a direct criterion. Whether these patterns mark emissions remains untested; locating emissions is a future application requiring paired gas measurements.