Processing Hyperspectral Data in Machine Learning

The adaptive and automated analysis of hyperspectral data is mandatory in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, medicine, biochemistry, engineering, and others. Hyperspectra di er from other spectral data that a large frequency range is uniformly sampled. The resulting discretized spectra have a huge number of spectral bands and can be seen as good approximations of the underlying continuous spectra. The large dimensionality causes numerical di culties in e cient data analysis. Another aspect to deal with is that the amount of data may range from several billion samples in geophysics to only a few in medical applications. In consequence, dedicated machine learning algorithms and approaches are required for precise while e cient processing of hyperspectral data, which should include also expert knowledge of the application domain as well as mathematical properties of the hyperspectral data.