Mineral Identification and Classification by Combining Use of Hyperspectral VNIR/SWIR and Multispectral TIR Remotely Sensed Data

Abstract

The combining of the hyperspectral visible/near-infrared (VNIR) and shortwave infrared (SWIR) data with the hyperspectral thermal infrared (TIR) data has been recognized to be an effective way for the detailed identification and classification of minerals. However, what are the effects of introduction of multispectral TIR data on the minerals identification and classification and how those effects are, are not well studied in the background of the majority of multispectral TIR data. To fully evaluate the effects of the combining of the hyperspectral VNIR-SWIR reflectivity with the multispectral thermal infrared (TIR) emissivity, this paper tries to use the real data to testify the practicability of introduction of multispectral TIR data for the accuracies of mineral identification and classification. Four classifiers are selected in the experiment. Compared with the results using hyperspectral data alone, the introducing of multispectral TIR data in identification and classification has improved accuracies. However, the overall accuracies are improved about 1–5% by using different classifiers. Although those improvements are not well obvious due to the low spatial resolution, where the spectral mixture of various minerals exists, and the retrieval error of land surface emissivity, the multispectral TIR data are still effective supplements for hyperspectral VNIR and SWIR data in mineral identification and classification.