A review on spectral processing methods for geological remote sensing

• Spectral analysis methods available to geologic remote sensing are reviewed.
• The processing methods are categorized into knowledge-based and data-driven approach.
• Using a case study, the effectiveness of processing techniques are demonstrated.
• Hybridization of the two approaches is shown to yield robust processing algorithm.

In this work, many of the fundamental and advanced spectral processing methods available to geologic remote sensing are reviewed. A novel categorization scheme is proposed that groups the techniques into knowledge-based and data-driven approaches, according to the type and availability of reference data. The two categories are compared and their characteristics and geologic outcomes are contrasted. Using an oil-sand sample scanned through the sisuCHEMA hyperspectral imaging system as a case study, the effectiveness of selected processing techniques from each category is demonstrated. The techniques used to bridge between the spectral data and other geoscience products are then discussed. Subsequently, the hybridization of the two approaches is shown to yield some of the most robust processing techniques available to multi- and hyperspectral remote sensing. Ultimately, current and future challenges that spectral analysis are expected to overcome and some potential trends are highlighted.