Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis

• Published in: Minerals (Basel) 2022-05, Vol.12 (5), p.508
• Main Authors: Xie, Busheng, Mao, Wenfei, Peng, Boqi, Zhou, Shengyu, Wu, Lixin
• Format: Article
• Language: English
• Subjects: Accuracy; Analysis; Analytical methods; Biotite; Classification; Data processing; Earth surface; Emission measurements; Emission spectra; Emittance; Feature extraction; Feldspars; Fourier transforms; Geologic mapping; Geological mapping; Geology; Identification; Igneous rocks; Information processing; Infrared analysis; Infrared spectra; Infrared spectrometers; Libraries; Lithology; Mineral composition; Minerals; Near infrared radiation; Principal components analysis; Radiation; Remote sensing; Rocks; Short wave radiation; Spectra; Spectral analysis; Spectral emittance; Spectral signatures; Spectroscopy; Spectrum analysis
• ISSN: 2075-163X; 2075-163X
• DOI: 10.3390/min12050508

Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images.