Spectral Domain Noise Suppression in Dual-Sensor Hyperspectral Imagery Using Gaussian Processes

The use of hyperspectral data is limited, in part, by increased spectral noise, particularly at the extremes of the wavelength ranges sensed by scanners. We apply Gaussian Processes (GPs) as a preprocessing step prior to extracting mineralogical information from the image using automated feature extraction. GPs are a probabilistic machine learning technique that we use for suppressing noise in the spectral domain. The results demonstrate that this approach leads to large reductions in the amount of noise, leading to major improvements in our ability to automatically quantify the abundance of iron and clay minerals in hyperspectral data acquired from vertical mine faces.