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Open AccessArticle

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

1
School of Instrumentation Science and Opto-Electronic Engineering, Beihang University, Beijing 100191, China
2
School of Engineering, Newcastle University, Newcastle NE1 7RU, UK
*
Authors to whom correspondence should be addressed.
Sensors 2019, 19(2), 328; https://doi.org/10.3390/s19020328
Received: 5 December 2018 / Revised: 11 January 2019 / Accepted: 11 January 2019 / Published: 15 January 2019
(This article belongs to the Section Remote Sensors)
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. View Full-Text
Keywords: hyperspectral imaging model; application capability; quantitative evaluation; normalized average distance hyperspectral imaging model; application capability; quantitative evaluation; normalized average distance
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MDPI and ACS Style

Li, N.; Huang, X.; Zhao, H.; Qiu, X.; Deng, K.; Jia, G.; Li, Z.; Fairbairn, D.; Gong, X. A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping. Sensors 2019, 19, 328. https://doi.org/10.3390/s19020328

AMA Style

Li N, Huang X, Zhao H, Qiu X, Deng K, Jia G, Li Z, Fairbairn D, Gong X. A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping. Sensors. 2019; 19(2):328. https://doi.org/10.3390/s19020328

Chicago/Turabian Style

Li, Na; Huang, Xinchen; Zhao, Huijie; Qiu, Xianfei; Deng, Kewang; Jia, Guorui; Li, Zhenhong; Fairbairn, David; Gong, Xuemei. 2019. "A Combined Quantitative Evaluation Model for the Capability of Hyperspectral Imagery for Mineral Mapping" Sensors 19, no. 2: 328. https://doi.org/10.3390/s19020328

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