Abstract:

Spectral feature is the physical basis of rock identification. In order to remove the rock spectral noise, the spectra including common 15 rock samples belonging to 10 rock types are pretreated by averaging, resampling, smoothing, and fitting the value of water vapor absorption band. The continuum removing methods are used to obtain absorption-band parameters of spectra. Among the rock samples, the mica slate’s absorption feature is the most obvious. The normalized data studied by using R-mode principle factor method shows that the first principal factor axis represents the major absorption spectra of cations, anions and water vapor, and the second represents a small number of cation band. The characteristic spectral bands of the rock are 385-525 nm, 735-1 365 nm, 1 435-1 785 nm, 1 890-1 952 nm and 1 995-2 310 nm.The physical meanings of these bands are also identified. The rock spectra are classified into four types by two-dimensional image analysis. From first type to last type, the spectra prove the gradually shallow absorption depth, the decreasing area, the gradually narrowing width and the increasing number of absorption peaks.Iron and pelitization alteration phenomenon are obvious. The classification results verified by cluster analysis are of better correspondence. A physical basis for rock classification and identification are provided by remote sensing technology. It is of significance to abstract effectively hyper spectral data and classify hyper spectra images.

Key words: rocks, continuum removed, spectrum analysis, correspondence analysis, classification