Nonlinear Mixing Characteristics of Reflectance Spectra of Typical Mineral Pigments

Hyperspectral technology has been used to identify pigments that adhere to the surfaces of polychrome artifacts. However, the colors are often produced by the mixing of pigments, which requires that the spectral characteristics of the pigment mixtures be considered before pigment unmixing is conducted. Therefore, we proposed an experimental approach to investigate the nonlinear degree of spectral reflectance, using several mixing models, and to evaluate their performances in the study of typical mineral pigments. First, five mineral pigments of azurite, malachite, cinnabar, orpiment, and calcite were selected to form five groups of samples, according to their different mass ratios. Second, a fully constrained least squares algorithm based on the linear model and three algorithms based on the nonlinear model were employed to calculate the proportion of each pigment in the mixtures. We evaluated the abundance accuracy as well as the similarity between the measured and reconstructed spectra produced by those mixing models. Third, we conducted pigment unmixing on a Chinese painting to verify the applicability of the nonlinear model. Fourth, continuum removal was also introduced to test the nonlinearity of mineral pigment mixing. Finally, the results indicated that the spectral mixing of different mineral pigments was more in line with the nonlinear mixing model. The spectral nonlinearity of mixed pigments was higher near to the wavelength corresponding to their colors. Meanwhile, the nonlinearity increased with the wavelength increases in the shortwave infrared bands.

Thermal Infrared Spectral Characteristics of Bunker Fuel Oil to Determine Oil-Film Thickness and API

Remote sensing is an important method for monitoring marine oil-spill accidents. However, methods for measuring oil-film thickness remain insufficient. Due to the stable differences in the surface emissivity and temperature of oil and water, the oil film can be detected using thermal infrared. This study measured emissivity of seven different oil-film thicknesses and seven different American Petroleum Institute (API) densities, and analyzed the spectral characteristics. Results show an optimal wavelength position for oil-film thickness and fuel API density monitoring is 12.55 μm. Principal component analysis and continuum removal methods were used for data processing. Stepwise multiple linear regression was used to establish relationships between emissivity and oil slick thicknesses and API densities. Oil-film thickness and fuel API density data were analyzed by principal component analysis and continuum removal before regression analysis. The spectral emissivity data was convolved into Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Very High Resolution Radiometer (AVHRR) thermal bands to determine potential of the sensor in oil-film detection. The result shows that neither could be used to estimate thickness. The AVHRR-4 band and band 12 and 13 of the ASTER could be used to separate oils from water and have potential to distinguish different oil types.

DIFFERENT STATISTICAL METHODS FOR THE DISCRIMINATION OF TROPICAL MANGROVE SPECIES USING IN-SITU HYPERSPECTRAL DATA

The study highlights the hyperspectral characteristics of canopies of 14 tropical mangrove species, belonging to nine families found in the tidal forests of the Indian Sundarbans. Hyperspectral observations were recorded using a field spectroradiometer, pre-processed and subjected to derivative analysis and continuum removal. Mann-Whitney U tests were applied on the spectral data in four spectral forms: (i) Reflectance Spectra (ii) First Derivative, (iii) Second Derivative and (iv) Continuum Removal Reflectance Spectra. Factor analysis was applied in each of the spectral forms for feature reduction and identification of the important wavelengths for species discrimination. Stepwise discriminant analysis was used on the feature reduced reflectance spectra to obtain optimal bands for computation of Jeffries–Matusita distance. The Mann-Whitney U test could be satisfactorily used for determining the significant (separable) bands for discriminating the species. In general, the red region, red edge domain, specific near infrared bands (including 759, 919, 934, 940, 948, 1152, 1156, 1159 and 1212 nm) and shortwave infrared region (1503–1766 nm) played major roles in spectral separability. Overall, hyperspectral data showed potential for discriminating between mangrove canopies of different species and the results of the study also indicated the usefulness of the applied statistical tools for discrimination.