The stability of the spectrum parameters (width, depth and shape) in a mineral spectrum identification under different influencing factors is greatly influenced by the identification effects. It is shown that the positions of the peak and the valley of different minerals in the characteristic spectrum are more stable, and they are relatively stable characteristic parameters of the spectrum. This paper proposes a hyperspectral mineral identification algorithm based on the characteristic spectrum peak-valley correlation coefficient method, and the mathematical model and the operation flowchart of extracting the spectral stability parameters (the locations of the peak and the valley) are established. The algorithm is based on the extraction of the reference spectra peak-valley positions, and the calculations of the peaks and the valleys of minerals characteristic spectrum and the correlation coefficient of the corresponding measured mineral spectrum, to determine whether they exceed the thresholds, as the main basis of comparison of the similarity degree of mineral spectra. Gansu Beishan Shijinpo gold mining is taken as the study area, using the CAIS/SASI airborne hyperspectral data, and the algorithm is used to identify the regions of alteration minerals, and the results are compared with those obtained with the existing typical algorithms (SFF、SID、SAM). It is shown that the correct recognition rate of the algorithm is higher, and the accuracy of the algorithm can reach 85%.

hyperspectral remote sensing  /  characteristic spectrum  /  peak-valley correlation coefficient method  /  mineral information identification