Fine mineral identification of GF-5 hyperspectral image

Mineral identification, which is a feature of hyperspectral remote sensing technology, has been widely applied in geoscience and has achieved remarkable application results in geological and mineral fields. With the improvement of spectral resolution, mineral identification has gradually developed from the identification of mineral species to the identification of fine information, such as mineral subclasses and mineral components. Fine mineral information is extremely important in applications, such as the prediction and evaluation of mineral resources and geological environment indication. It directly affects the breadth and depth of hyperspectral remote sensing geological application. Spectral resolution and mineral identification methods are the main factors in fine mineral identification. GF-5 has 330 bands at the spectral range of 350-2500 nm, and its spectral resolution is higher than 10 nm. Its ultrahigh spectral resolution provides the possibility for fine mineral identification. In this study, a mineral identification method was presented on the basis of spectral characteristic enhancement matching degree and characteristic parameters by summarizing and analyzing the advantages and disadvantages of two commonly used mineral identification methods, namely, spectral matching and characteristic parameters, and combining the characteristics of GF-5 hyperspectral data. The proposed method was applied to conduct mineral identification in Liuyuan, Gansu, and Cuprite, USA. The mineral types and subclasses were first identified, and then the information on sericite composition was reversed. The airborne hyperspectral data were compared with the mapping results of GF-5. The results show that the GF-5 mineral identification information distribution has a good consistency with airborne HyMap and AVIRIS, and the average accuracy of GF-5 mineral identification is 90% higher compared with the airborne data. The accuracy rate, as a holistic evaluation, only serves as a reference because of the relatively limited statistical data, uneven distribution of mineral information, and the difference in original spatial resolution. The comparison results show that the proposed mineral identification method can meet the requirements of GF-5 mineral fine identification. Ultrahigh spectral resolution makes GF-5 advantageous in the identification of mineral composition information and distinguishing minerals with high spectral similarity. The proposed mineral identification method based on spectral characteristic enhancement matching degree and characteristic parameters can provide technical support for subsequent operational applications. © 2020, Science Press. All right reserved.