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A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping

Lookup NU author(s): Professor Zhenhong Li, Dr David Fairbairn



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


To analyze the influence factors of hyperspectral remote sensing data processing, and quantitatively evaluate the application capability of hyperspectral data, a combined evaluation model based on the physical process of imaging and statistical analysis was proposed. The normalized average distance between different classes of ground cover is selected as the evaluation index. The proposed model considers the influence factors of the full radiation transmission process and processing algorithms. First- and second-order statistical characteristics (mean and covariance) were applied to calculate the changes for the imaging process based on the radiation energy transfer. The statistical analysis was combined with the remote sensing process and the application performance, which consists of the imaging system parameters and imaging conditions, by building the imaging system and processing models. The season (solar zenith angle), sensor parameters (ground sampling distance, modulation transfer function, spectral resolution, spectral response function, and signal to noise ratio), and number of features were considered in order to analyze the influence factors of the application capability level. Simulated and real data collected by Hymap in the Dongtianshan area (Xinjiang Province, China), were used to estimate the proposed model’s performance in the application of mineral mapping. The predicted application capability of the proposed model is consistent with the theoretical analysis.

Publication metadata

Author(s): Li N, Huang X, Zhao H, Qiu X, Deng K, Jia G, Li Z, Fairbairn D, Gong X

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2019

Volume: 19

Issue: 2

Online publication date: 15/01/2019

Acceptance date: 11/01/2019

Date deposited: 20/01/2019

ISSN (electronic): 1424-8220

Publisher: MDPI AG


DOI: 10.3390/s19020328


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