Search

• Use of N-Dimensional Spectral Solid Angle (NSSA) for band selection. • Guidelines for the selection of two key parameters for NSSA band selection. • Band selection for the improved discrimination of spectrally similar materials. • Bands capturing absorption feature position, shape, and depth. Selecting a subset of bands from hyperspectral data can improve the discrimination of ground targets because the most distinguishing spectral features are utilized. Targets with similar spectra are particularly challenging for band selection. A band selection method using the N-dimensional Solid Spectral Angle (NSSA) was recently proposed by Tian et al. (2016) to select the most dissimilar spectral regions amongst targets, but no case studies have been conducted using data from natural targets and there are currently no guidelines for the parameter selection in the NSSA band selection method. This study uses two spectral datasets of geologic relevance (clay minerals and ultramafic rocks), each with spectrally similar materials, to establish guidelines for the selection of two parameters (k and threshold) that will enable the use of the method for practical applications. K defines the band interval (relates to feature width) from which NSSA is calculated, and the threshold defines the number of bands selected from a profile of NSSA as a function of wavelength. The first guideline consists in constraining the maximum k value based on the spectral dimensionality of the widest significant spectral feature for the materials under study. The second guideline is to use a profile of the NSSA value as a function of wavelength for each permissible k value to capture the primary wavelength regions of high NSSA values. Finally, the threshold parameter for each k is estimated from a graph of the NSSA value as a function of the number of bands. The guidelines on the parameter definition allow non-expert users to select a subset of bands while capturing both narrow and broad discriminating features. Results show that bands selected from the two datasets are in good agreement with known spectral features. Of significance is that the bands encompass a range of distinguishing and often subtle spectral characteristics that include absorption feature position, and shape (asymmetry) and depth, the same that are recognized by experts, and thus can be used to assist experts in identifying key features. Moreover, the bands selected with NSSA show improved class separation as illustrated for datasets of spectrally similar materials. When tested for the discrimination of clay minerals, a competitive method named Variable Number Variable Band Selection (VNVBS) did not provide adequate information for the selection of bands. There are several valuable band selection methods reported in the literature, but few can be applied to datasets encompassing a relatively small number of spectra and to select bands that enable the discrimination of spectrally similar materials. As demonstrated in this study, the NSSA method should be of great value to studies that require feature identification from spectral libraries either resulting from the collection of field spectra or the extraction of endmembers from imagery. [ABSTRACT FROM AUTHOR]

Clay minerals play a crucial role in the processability of oil sands ores and in the management of tailings. An increase in fine content generally leads to a decrease in both bitumen recovery performance and tailings settling rate. It is thus important to identify clay types and their abundance in oil sands ores and tailings. This study made use of oil sands samples characterized for quantitative mineralogy by x-ray diffraction, to gain an understanding of changes in the reflectance spectra of oil sands. The sample suite included bitumen-removed oil sands ore samples and their different fine size fractions. Spectral metrics applicable to the prediction of quartz and clay contents in oil sands were then derived with a focus on metrics correlating with sample content in total 2:1 clays (total of illite and illite-smectite) and kaolinite. Metrics in the shortwave infrared (SWIR) and longwave infrared (LWIR) were found to correlate with mineral contents. The best predictions of clays and quartz were achieved using LWIR metrics (R 2 > 0.89). Results also demonstrated the applicability of LWIR metrics in the prediction of kaolinite and total 2:1 clays. [ABSTRACT FROM AUTHOR]

Clay minerals constitute a significant fraction of oil sands tailings; and clay mineralogy, abundance, and morphology can dramatically affect the strength of the tailings during consolidation. The ability of clays to expand or swell in the presence of water is of importance in establishing the post-depositional strength and the geotechnical stability of a tailings deposit for reclamation purposes. The surface activity of oil sands tailings solids is typically determined using the methylene blue index (MBI), an analytical technique that quantifies the ionic absorption capacity of a sample. The present work explores the use of short-wave infrared (SWIR) and long-wave infrared (LWIR) hyperspectral observations for the estimation of the swelling potential or activity of oil sands tailings indicated by MBI. Spectral features in reflectance spectra were characterized in the SWIR and LWIR, in particular those attributed to the presence of quartz and clays. These features were employed to assess the correlation between MBI and remote sensing reflectance measurements collected in the laboratory from air-dried tailings. In the SWIR, a band ratio of reflectance at 2.111 and 1.992 μm was highly correlated with MBI. For the potential estimation of MBI in outdoor settings, where the intervening atmosphere can impact the spectral measurements, a band ratio of reflectance at 1.773 and 1.307 μm provided an estimation of MBI when moisture of tailings did not exceed 0.20 g/g (20 wt%). The best predictions of MBI were obtained in the LWIR using the reflectance peaks at 9.67 µm and 11 µm attributable to clay minerals. [ABSTRACT FROM AUTHOR]