Automated Multi-class Classification of Remotely Sensed Hyperspectral Imagery Via Gaussian Processes with a Non-stationary Covariance Function

摘要

The ability to automatically classify hyperspectral imagery is of fundamental economic importance to the mining industry. A method of automated multi-class classification based on multi-task Gaussian processes (MTGPs) is proposed for classification of remotely sensed hyperspectral imagery. It is proved that because of the illumination invariance of the hyperspectral curves, the covariance function of the Gaussian process (GPs) has to be non-stationary. To enable multi-class classification of the hyperspectral imagery, a non-stationary multi-task observation angle-dependent covariance function is derived. In order to test MTGP, it was applied to data acquired in the laboratory and also in field. First, the MTGP was applied to hyperspectral imagery acquired under artificial light from samples of rock of known mineral composition. Data from a high-resolution field spectrometer are used to train the GPs. Second, the MTGP was applied to imagery of a vertical rock wall acquired under natural illumination. Spectra from hyperspectral imagery acquired in the laboratory are used to train the GPs. Results were compared with those obtained using the spectral angle mapper (SAM). In laboratory imagery, MTGP outperformed SAM across several metrics, including overall accuracy (MTGP: 0.96-0.98; SAM: 0.91-0.93) and the kappa coefficient of agreement (MTGP: 0.95-0.97; SAM: 0.88-0.91). MTGP applied to hyperspectral imagery of the rock wall gave broadly similar results to those from SAM; however, there were important differences. Some rock types were confused by SAM, but not by MTGP. Comparison of classified imagery with ground truth maps showed that MTGP outperformed SAM.