Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Preprocessing
2.3. Spectral Transform
2.4. Spectral Variable Selection
2.5. Model Establishment and Accuracy Evaluation
2.6. Flowchart
3. Results
3.1. Element Content and Visible-Near-Infrared Spectral Reflectance
3.2. Correlation between Different Transform Spectra and Cu Content
3.3. PLS Model of Different Transform Spectra
3.4. PLS Models of Different Band Selection Methods
3.5. HySpex Imaging Hyperspectral Cu Content Extraction
4. Discussion
5. Conclusions
- (1)
- Spectral transformation technology can highlight the band that is characteristically reflected by the element content, thereby improving the predictive ability of the model;
- (2)
- The 20 characteristic bands selected from the transform spectrum by the CARS method were input as independent variables into the PLS method to construct the detritus copper content inversion model with the highest accuracy. R2 (0.7342) was highest and MAE (19.926) was lowest in the verification set, indicating that the HySpex pixel spectrum could be used to quickly and accurately estimate the copper content in detritus;
- (3)
- GA, CARS, and SPA can be used for quickly selecting feature bands, and the use of these feature bands for modeling can simplify the model complexity and improve prediction accuracy. CARS is the optimal feature band screening method; it reduced the complexity of the model to the greatest extent and improved the stability of the model while ensuring the accuracy of the inversion, and has a wider application prospect;
- (4)
- Ultra-low HySpex imaging hyperspectral data have high spatial and spectral resolutions, but there were problems with information redundancy. Adopting appropriate spectral transformation technology and band selection methods to improve the prediction accuracy and data-processing efficiency can provide a low-cost and efficient method for the delineation and reduction of key mining research areas in the future.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Sensor Name | VNIR-1024 | Sensor Image |
---|---|---|
Detector | SiCCD 2048 × 2048 | |
Spectral range | 400–1000 nm | |
Spatial pixels | 1024 | |
Field of view angle | 17° | |
Extension lens | 34° | |
Instantaneous field of view | 0.18 mrad/0.36 mrad | |
Spectral sampling | 2.8 nm | |
Spectral number | 216 | |
Camera weight | 4.6 kg | |
Camera size(cm) | 31.5 × 8.4 × 13.8 | |
Power consumption | ~6 W |
Spectral Transformation | Formula |
---|---|
Reciprocal | |
Logarithmic | |
Power | |
Envelope removal | |
First-order derivatives | |
Second-order derivatives | |
Power-logarithmic | |
Logarithmic-power |
Spectral Transformations | Number of Principal Components | Validation Set | |||
---|---|---|---|---|---|
RRMSEP | MAE | ||||
R | 17 | 0.5048 | 0.4860 | 2.133 | 22.774 |
(R)′ | 11 | 0.3474 | 0.3167 | 1.906 | 25.949 |
(R)″ | 5 | 0.2695 | 0.2612 | 1.761 | 27.755 |
15 | 0.5307 | 0.4905 | 2.137 | 22.712 | |
1/R | 4 | 0.2725 | 0.2714 | 0.804 | 29.705 |
CR(R) | 13 | 0.3834 | 0.3634 | 1.638 | 22.829 |
17 | 0.5201 | 0.4901 | 2.127 | 22.690 | |
17 | 0.5913 | 0.5863 | 2.064 | 21.405 | |
17 | 0.5869 | 0.5628 | 2.079 | 23.035 |
Spectral Transformations | Number of Bands | Number of Principal Components | Determination Coefficient of Training Set (R2) | Validation Set | ||
---|---|---|---|---|---|---|
R2 | RRMSEP | MAE | ||||
GA | 105 | 18 | 0.536 | 0.519 | 2.102 | 23.171 |
CARS | 20 | 12 | 0.751 | 0.734 | 2.21 | 19.926 |
SPA | 42 | 18 | 0.709 | 0.691 | 2.175 | 21.764 |
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Jiang, G.; Zhou, S.; Cui, S.; Chen, T.; Wang, J.; Chen, X.; Liao, S.; Zhou, K. Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content. Sensors 2020, 20, 6325. https://doi.org/10.3390/s20216325
Jiang G, Zhou S, Cui S, Chen T, Wang J, Chen X, Liao S, Zhou K. Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content. Sensors. 2020; 20(21):6325. https://doi.org/10.3390/s20216325
Chicago/Turabian StyleJiang, Guo, Shuguang Zhou, Shichao Cui, Tao Chen, Jinlin Wang, Xi Chen, Shibin Liao, and Kefa Zhou. 2020. "Exploring the Potential of HySpex Hyperspectral Imagery for Extraction of Copper Content" Sensors 20, no. 21: 6325. https://doi.org/10.3390/s20216325