Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Hyperspectral Image Acquisition and Preprocessing
2.3. UAV Survey and DEM Processing
2.4. Ground Truthing of the Outcrop
2.5. Spectral Analysis
2.6. Band Selection and Spectral Index Derivation
2.7. Fusion of Hyperspectral Imaging and UAV-Based 3D Model
3. Results and Discussion
3.1. UAV-Based Orthorectified Image and Digital Elevation Model of the Outcrop
3.2. Spectral Characteristics of Limestone and Dolostone
3.3. Carbonate Rock Classification
3.3.1. Carbonate Rock Classification by Random Forest Classification
3.3.2. Band Selection and Derivation of Carbonate Rock Indices from Binary Logistic Regression
3.4. Fusion of Classification Map and UAV-Based 3D Model
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
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Spectral range | 930–2500 nm |
Spatial pixels | 384 |
Spectral channels | 288 |
Spectral sampling | 5.45 nm |
FOV | 16° |
Pixel FOV across/along | 0.73/0.73 mrad |
Bit resolution | 16 bit |
Noise floor | 150 e |
Dynamic range | 7500 |
Peak SNR (at full resolution) | >1100 |
Max speed | 400 fps |
Power consumption | 30 W |
Dimensions (l–w–h) | 38–12–17.5 cm |
Weight | 5.7 kg |
Class | Training Data | |||
User’s Accuracy (%) | Producer’s Accuracy (%) | Commission Error (%) | Omission Error (%) | |
Limestone | 95.12 | 95.51 | 4.88 | 4.49 |
Dolostone | 96.41 | 95.17 | 3.59 | 4.83 |
Overall Accuracy: 95.34% Kappa Coefficient: 0.91 | ||||
Class | Validation Data | |||
User’s Accuracy (%) | Producer’s Accuracy (%) | User’s Accuracy (%) | Omission Error (%) | |
Limestone | 93.5 | 92.38 | 6.5 | 7.62 |
Dolostone | 95.04 | 91.03 | 4.96 | 8.97 |
Overall Accuracy: 91.71% Kappa Coefficient: 0.89 |
Wavelength (nm) | β1 | S.E. 2 | Wald 3 | Df 4 | Sig. 5 | Exp (β) 6 |
---|---|---|---|---|---|---|
Final selected spectral variables for limestone classification | ||||||
980 | 7.126 | 0.720 | 97.951 | 1 | 0 | 1244.091 |
1584 | −0.072 | 0.087 | 0.692 | 1 | 0.406 | 0.930 |
2290 | −149.158 | 60.089 | 6.162 | 1 | 0.013 | 0 |
2295 | 891.720 | 187.400 | 22.642 | 1 | 0 | 0 |
2300 | −1737.397 | 260.817 | 44.374 | 1 | 0 | 0 |
2306 nm | 1100.121 | 164.159 | 44.911 | 1 | 0 | 0 |
2316 nm | −468.074 | 116.805 | 16.058 | 1 | 0 | 0 |
2321 nm | 902.109 | 129.375 | 48.620 | 1 | 0 | 0 |
2331 nm | −1073.833 | 157.677 | 46.381 | 1 | 0 | 0 |
2336 nm | 1101.590 | 217.570 | 25.635 | 1 | 0 | 0 |
2341 nm | −1531.512 | 301.279 | 25.841 | 1 | 0 | 0 |
2346 nm | 1627.802 | 235.607 | 47.734 | 1 | 0 | 0 |
2352 nm | −967.948 | 95.835 | 102.012 | 1 | 0 | 0 |
2362 nm | 309.900 | 27.601 | 126.067 | 1 | 0 | 0 |
2480 nm | −19.540 | 2.481 | 62.014 | 1 | 0 | 0 |
Constant | −3.837 | 0.344 | 124.627 | 1 | 0 | 0.22 |
Final selected spectral variables for dolostone classification | ||||||
1968 nm | −27.184 | 6.085 | 19.960 | 1 | 0 | 0 |
2009 nm | 74.529 | 14.975 | 24.771 | 1 | 0 | 0 |
2024 nm | 104.447 | 20.916 | 24.936 | 1 | 0 | 0 |
2055 nm | −417.089 | 30.179 | 191.004 | 1 | 0 | 0 |
2091 nm | 173.937 | 24.084 | 52.157 | 1 | 0 | 0 |
2132 nm | 40.597 | 9.344 | 18.877 | 1 | 0 | 0 |
2300 nm | −24.935 | 4.797 | 27.021 | 1 | 0 | 0 |
2336 nm | −458.285 | 34.881 | 172.621 | 1 | 0 | 0 |
2341 nm | 537.312 | 35.762 | 225.745 | 1 | 0 | 0 |
Constant | −2.512 | 0.245 | 105.108 | 1 | 0 | 0.081 |
Parameters | Pseudo-R2 | Hosmer and Lemeshow Test | |||
---|---|---|---|---|---|
Cox and Snell R2 | Nagelkerke R2 | χ2 | df | p-Value | |
Limestone | 0.734 | 0.979 | 5.869 | 8 | 0.662 |
Dolostone | 0.712 | 0.956 | 17.533 | 8 | 0.07 |
Class | Validation Data | ||
---|---|---|---|
Overall Accuracy (%) | Commission Error (%) | Omission Error (%) | |
Limestone | 87.73 | 1.77 | 12.27 |
Dolostone | 88.02 | 0.22 | 11.98 |
Overall Accuracy: 87.91% Kappa Coefficient: 0.77 |
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Huynh, H.H.; Yu, J.; Wang, L.; Kim, N.H.; Lee, B.H.; Koh, S.-M.; Cho, S.; Pham, T.H. Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks. Remote Sens. 2021, 13, 3037. https://doi.org/10.3390/rs13153037
Huynh HH, Yu J, Wang L, Kim NH, Lee BH, Koh S-M, Cho S, Pham TH. Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks. Remote Sensing. 2021; 13(15):3037. https://doi.org/10.3390/rs13153037
Chicago/Turabian StyleHuynh, Huy Hoa, Jaehung Yu, Lei Wang, Nam Hoon Kim, Bum Han Lee, Sang-Mo Koh, Sehyun Cho, and Trung Hieu Pham. 2021. "Integrative 3D Geological Modeling Derived from SWIR Hyperspectral Imaging Techniques and UAV-Based 3D Model for Carbonate Rocks" Remote Sensing 13, no. 15: 3037. https://doi.org/10.3390/rs13153037