Identification of minerals in hyperspectral imagery based on the attenuation spectral absorption index vector using a multilayer perceptron

Various minerals present distinct spectra due to different constituent ions or groups. However, variable imaging conditions of hyperspectral imagery greatly affect the mineral spectra, which makes it difficult to identify minerals. Attenuation spectral absorption index (ASAI) is suggested to extract absorption features from mineral diagnostic bands, which combines spectral absorption index (SAI) with absorption position and resists variations in external factors. To integrate intrinsic features of mineral spectra, the ASAI vector is formed by the ASAIes of diagnostic absorption bands corresponding to all identified minerals, which depicts mineral contents to a certain extent. In this letter, a multilayer perceptron (MLP) model based on attenuation spectral absorption index vector (ASMLP) is proposed to obtain hierarchical features for accurate mineral identification. The hyperspectral data of the Nevada mining area collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are applied to illuminate the performance of the proposed model. The results show that ASMLP achieves an overall accuracy of 93.62% and outperforms other identification methods.

Automated summary

Different minerals show distinct spectra due to varying constituent ions or groups. The variable imaging conditions of hyperspectral imagery can greatly impact mineral spectra, making mineral identification challenging. By utilizing the attenuation spectral absorption index (ASAI) to extract absorption features from diagnostic bands, the proposed ASMLP model achieves a high overall accuracy of 93.62% in mineral identification, outperforming other methods.