Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data
<p>The location of study areas (<b>A</b>,<b>B</b>) and the distribution of field samples. The background images are from Gaofen-5 AHSI data© China Centre for Resources Satellite Data and Application (R: VNIR Band 59, G: VNIR Band 38, and B: VNIR Band 20). Elevation data are extracted from the shuttle radar topography mission (SRTM).</p> "> Figure 2
<p>Geological maps of the study areas (<b>A</b>,<b>B</b>). The scale of the mapping format is 1:500,000 [<a href="#B45-remotesensing-15-05334" class="html-bibr">45</a>].</p> "> Figure 3
<p>The FWHM of GF-5 AHSI data.</p> "> Figure 4
<p>The method flowchart of this study.</p> "> Figure 5
<p>1D-DWT of spectral signals (An represents the low-frequency coefficients after DWT, Dn represents the high-frequency coefficients after DWT, and n represents the number of decomposition levels).</p> "> Figure 6
<p>Wavelet function and scaling function of (<b>a</b>) haar, (<b>b</b>) db2, (<b>c</b>) db4, (<b>d</b>) db8, and (<b>e</b>) db10.</p> "> Figure 7
<p>Preprocessing process of GF-5 AHSI data.</p> "> Figure 8
<p>The mean image spectra for each rock unit in (<b>a</b>) study area A and (<b>b</b>) study area B.</p> "> Figure 9
<p>Original spectral curves and reconstructed curves of (<b>a</b>) dolostone, (<b>b</b>) andesite, (<b>c</b>) tuff, (<b>d</b>) limestone, (<b>e</b>) sandstone, and (<b>f</b>) granite. Curve db2_3_23 was shifted upward by 0.01, and curve db2_4_234 was shifted upward by 0.02. (Rock spectral curves using the left <span class="html-italic">y</span>-axis and high-frequency features after DWT using the right <span class="html-italic">y</span>-axis.)</p> "> Figure 9 Cont.
<p>Original spectral curves and reconstructed curves of (<b>a</b>) dolostone, (<b>b</b>) andesite, (<b>c</b>) tuff, (<b>d</b>) limestone, (<b>e</b>) sandstone, and (<b>f</b>) granite. Curve db2_3_23 was shifted upward by 0.01, and curve db2_4_234 was shifted upward by 0.02. (Rock spectral curves using the left <span class="html-italic">y</span>-axis and high-frequency features after DWT using the right <span class="html-italic">y</span>-axis.)</p> "> Figure 10
<p>(<b>a</b>) The original laboratory spectral curve and (<b>b</b>) the high-frequency curve after DWT (db2_4_1234) of granite samples. (Spectra were from ASD FieldSpec 3.)</p> "> Figure 11
<p>Mean of intra-class SAM in laboratory spectra.</p> "> Figure 12
<p>The standard deviation of intra-class SAM in laboratory spectra.</p> "> Figure 13
<p>OA corresponding to different ntree and mtry in the RF classifier.</p> "> Figure 14
<p>(<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>,<b>m,o</b>) are the original spectral curves, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>,<b>n,p</b>) are the high-frequency curves after DWT (db2_4_1234) of study area A. (Spectra were from GF-5 AHSI data.)</p> "> Figure 14 Cont.
<p>(<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>,<b>m,o</b>) are the original spectral curves, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>,<b>n,p</b>) are the high-frequency curves after DWT (db2_4_1234) of study area A. (Spectra were from GF-5 AHSI data.)</p> "> Figure 14 Cont.
<p>(<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i</b>,<b>k</b>,<b>m,o</b>) are the original spectral curves, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j</b>,<b>l</b>,<b>n,p</b>) are the high-frequency curves after DWT (db2_4_1234) of study area A. (Spectra were from GF-5 AHSI data.)</p> "> Figure 15
<p>(<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i,k</b>) are the original spectral curves, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j,l</b>) are the high-frequency curves after DWT (db2_4_1234) of study area B. (Spectra were from GF-5 AHSI data.)</p> "> Figure 15 Cont.
<p>(<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>,<b>i,k</b>) are the original spectral curves, and (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>,<b>j,l</b>) are the high-frequency curves after DWT (db2_4_1234) of study area B. (Spectra were from GF-5 AHSI data.)</p> "> Figure 16
<p>Mean values of intra-class SAM in study area A.</p> "> Figure 17
<p>The standard deviation of intra-class SAM in study area A.</p> "> Figure 18
<p>Mean values of intra-class SAM in study area B.</p> "> Figure 19
<p>The standard deviation of intra-class SAM in study area B.</p> "> Figure 20
<p>Lithological mapping results of study area A using (<b>a</b>) original spectra and (<b>b</b>) high-frequency features (haar_4_1234), with blue boxes indicating locations with significant differences in results.</p> "> Figure 21
<p>Lithological mapping results of study area B using (<b>a</b>) original spectra and (<b>b</b>) high-frequency features (haar_4_1234), with blue boxes indicating locations with significant differences in results.</p> "> Figure 22
<p>(<b>a</b>) The scatterplot of OA and the mean of SAM. (<b>b</b>) The scatterplot of F1-Score and the mean of SAM. (The lines in the figure are linear fitting lines.)</p> "> Figure 23
<p>Part of the collected tuff samples. (<b>a</b>) Breccia-crystalline clastic tuff, (<b>b</b>) andesite tuff, (<b>c</b>) breccia-crystalline clastic tuff, (<b>d</b>) and andesite-bearing breccia-crystalline clastic tuff.</p> "> Figure 24
<p>Black powder formed via coal weathering was found in some areas [<a href="#B44-remotesensing-15-05334" class="html-bibr">44</a>].</p> "> Figure A1
<p>Means and standard deviations for 5-fold cross-validation of laboratory spectral classification.</p> "> Figure A2
<p>Means and standard deviations for 5-fold cross-validation of image spectral classification.</p> ">
Abstract
:1. Introduction
2. Study Area and Data
2.1. Field Rock Samples for Laboratory Spectral Measurements
2.2. Study Area for Lithological Mapping
2.3. Gaofen-5
3. Methods
3.1. Laboratory Spectral Processing
3.1.1. Spectral Measurement
3.1.2. One-Dimensional Discrete Wavelet Transform
3.1.3. Random Forest Classification
3.2. Image Spectral Processing
3.2.1. GF-5 AHSI Data Preprocessing
3.2.2. One-Dimensional Discrete Wavelet Transform
3.2.3. Random Forest Classification and Accuracy Assessment
3.3. Evaluation of Intra-Class Variability
4. Results
4.1. Laboratory Spectral Classification
4.1.1. DWT of Spectral Curves
4.1.2. Intra-Class Variability
4.1.3. Classification Results
4.2. Hyperspectral Image Classification
4.2.1. Classification Accuracy Statistics
4.2.2. Intra-Class Variability
4.2.3. Lithological Mapping
Ground Truth (Percent) | |||||||||
---|---|---|---|---|---|---|---|---|---|
C1x | C1gd | C1y | Pξγ | PQg | P1aer | Pγδ | Pγδπ | ||
Predicted (Percent) | C1x | 80.1 (73.2) | 0.4 (0.1) | 7.3 (6.1) | 0.8 (5.3) | 0.7 (0.5) | 2.6 (3.4) | 0.6 (17.3) | 16.6 (1.7) |
C1gd | 0.4 (0.3) | 80.2 (66.4) | 5.1 (9.4) | 2.2 (1.4) | 0.3 (0.8) | 1.1 (5.6) | 14.2 (0.5) | 2.1 (18.0) | |
C1y | 6.0 (1.2) | 0.7 (12.3) | 48.8 (43.1) | 6.6 (4.9) | 1.4 (1.6) | 7.9 (9.0) | 2.8 (3.8) | 5.5 (7.2) | |
Pξγ | 1.7 (4.0) | 2.3 (2.1) | 9.4 (9.5) | 76.4 (66.8) | 8.2 (8.8) | 7.8 (14.1) | 8.0 (8.8) | 1.6 (1.7) | |
PQg | 1.6 (0.3) | 0.3 (1.9) | 5.3 (5.7) | 3.4 (8.2) | 86.6 (85.0) | 5.7 (6.3) | 2.9 (3.1) | 0.9 (1.1) | |
P1aer | 1.0 (1.8) | 0.1 (2.8) | 6.5 (7.1) | 6.0 (5.9) | 2.4 (1.7) | 70.0 (50.5) | 1.3 (3.8) | 2.9 (5.8) | |
Pγδ | 0.9 (18.5) | 15.1 (0.5) | 11.2 (12.6) | 4.7 (7.2) | 0.4 (1.3) | 4.0 (7.4) | 70.0 (61.4) | 3.8 (3.6) | |
Pγδπ | 8.3 (0.6) | 1.0 (13.9) | 6.4 (6.3) | 0.1 (0.4) | 0.0 (0.2) | 0.9 (3.7) | 0.3 (1.5) | 66.7 (61.0) | |
OA = 69.94% (63.76%) | Kappa = 0.62 (0.55) |
Ground Truth (Percent) | |||||||
---|---|---|---|---|---|---|---|
(E3-N1)t | Qp2W | Qp3X | Cγ | Cξγ | C2wt | ||
Predicted (Percent) | (E3-N1)t | 89.2 (79.8) | 13.7 (16.6) | 5.0 (7.2) | 1.6 (5.8) | 0.2 (0.1) | 0.3 (0.4) |
Qp2W | 8.6 (15.2) | 81.8 (75.2) | 4.4 (4.9) | 0.8 (3.5) | 0.8 (2.1) | 0.4 (1.3) | |
Qp3X | 1.4 (1.8) | 3.5 (4.7) | 81.6 (71.8) | 3.7 (3.6) | 4.0 (10.2) | 1.6 (3.5) | |
Cγ | 0.9 (3.2) | 1.0 (3.3) | 4.7 (9.5) | 84.9 (79.8) | 3.7 (4.3) | 8.1 (7.8) | |
Cξγ | 0.0 (0.0) | 0.0 (0.2) | 3.8 (5.7) | 1.3 (1.3) | 87.0 (77.4) | 3.0 (5.4) | |
C2wt | 0.0 (0.0) | 0.0 (0.1) | 0.5 (0.8) | 7.8 (6.0) | 4.3 (6.0) | 86.5 (81.6) | |
OA = 84.13% (77.34%) | Kappa = 0.80 (0.71) |
4.3. Relationship between OA and Intra-Class Variability
5. Discussion
5.1. Laboratory Spectral Classification
5.2. Hyperspectral Image Classification
5.3. Limitations and Strengths of This Study
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
- Radford, D.D.G.; Cracknell, M.J.; Roach, M.J.; Cumming, G.V. Geological Mapping in Western Tasmania Using Radar and Random Forests. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2018, 11, 3075–3087. [Google Scholar] [CrossRef]
- Rezaei, A.; Hassani, H.; Moarefvand, P.; Golmohammadi, A. Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods. Geol. Ecol. Landsc. 2020, 4, 59–70. [Google Scholar] [CrossRef]
- El-Omairi, M.A.; El Garouani, A. A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data. Heliyon 2023, 9, e20168. [Google Scholar] [CrossRef] [PubMed]
- Windeler, D.S. Garnet-pyroxene alteration mapping in the Ludwig skarn (Yerington, Nevada) with geoscan airborne multispectral data. Photogramm. Eng. Remote Sens. 1993, 59, 1277–1286. [Google Scholar]
- Greenbaum, D. Lithological discrimination in central Snowdonia using airborne multispectral scanner imagery. Int. J. Remote Sens. 1987, 8, 799–816. [Google Scholar] [CrossRef]
- Nguemhe Fils, S.C.; Bekele Mongo, C.H.; Nkouathio, D.G.; Mimba, M.E.; Etouna, J.; Njandjock Nouck, P.; Nyeck, B. Radarsat-1 image processing for regional-scale geological mapping with mining vocation under dense vegetation and equatorial climate environment, Southwestern Cameroon. Egypt. J. Remote Sens. Space Sci. 2018, 21, S43–S54. [Google Scholar] [CrossRef]
- Bentahar, I.; Raji, M. Comparison of Landsat OLI, ASTER, and Sentinel 2A data in lithological mapping: A Case study of Rich area (Central High Atlas, Morocco). Adv. Space Res. 2021, 67, 945–963. [Google Scholar] [CrossRef]
- Alibegovic, G.; Schut, A.G.T.; Wardell-Johnson, G.W.; Robinson, T.P. Seasonal differences assist in mapping granite outcrops using Landsat TM imagery across the Southwest Australian Floristic Region. J. Spat. Sci. 2015, 60, 37–49. [Google Scholar] [CrossRef]
- Pal, M.K.; Rasmussen, T.M.; Abdolmaleki, M. Multiple multi-spectral remote sensing data fusion and integration for geological mapping. In Proceedings of the 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands, 24–26 September 2019; pp. 1–5. [Google Scholar]
- Rowan, L.C.; Mars, J.C.; Simpson, C.J. Lithologic mapping of the Mordor, NT, Australia ultramafic complex by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Remote Sens. Environ. 2005, 99, 105–126. [Google Scholar] [CrossRef]
- Thannoun, R.G. Mapping lithological and mineralogical units using hyperspectral imagery. Malays. J. Sci. 2021, 40, 93–106. [Google Scholar] [CrossRef]
- Guo, X.; Li, P.; Li, J. Lithological mapping using EO-1 Hyperion hyperspectral data and semisupervised self-learning method. J. Appl. Remote Sens. 2021, 15, 032209. [Google Scholar] [CrossRef]
- Zhang, Y.; Pan, W.; Yu, Z. Application of Gaofen-5 hyperspectral data in uranium exploration: A case study of Weijing in Inner Mongolia, China. In Proceedings of the SPIE, Ninth Symposium on Novel Photoelectronic Detection Technology and Applications, Hefei, China, 2–4 November 2022; Volume 12617. [Google Scholar]
- Shebl, A.; Abriha, D.; Fahil, A.S.; El-Dokouny, H.A.; Elrasheed, A.A.; Csámer, Á. PRISMA hyperspectral data for lithological mapping in the Egyptian Eastern Desert: Evaluating the support vector machine, random forest, and XG boost machine learning algorithms. Ore Geol. Rev. 2023, 161, 105652. [Google Scholar] [CrossRef]
- Peyghambari, S.; Zhang, Y. Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review. J. Appl. Remote Sens. 2021, 15, 031501. [Google Scholar] [CrossRef]
- Pour, A.B.; Hashim, M. ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration. SpringerPlus 2014, 3, 130. [Google Scholar] [CrossRef]
- Hu, B.; Xu, Y.; Wan, B.; Wu, X.; Yi, G. Hydrothermally altered mineral mapping using synthetic application of Sentinel-2A MSI, ASTER and Hyperion data in the Duolong area, Tibetan Plateau, China. Ore Geol. Rev. 2018, 101, 384–397. [Google Scholar] [CrossRef]
- Liu, L.; Zhou, J.; Han, L.; Xu, X. Mineral mapping and ore prospecting using Landsat TM and Hyperion data, Wushitala, Xinjiang, northwestern China. Ore Geol. Rev. 2017, 81, 280–295. [Google Scholar] [CrossRef]
- Chen, X.; Warner, T.A.; Campagna, D.J. Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada: A rule-based system. Int. J. Remote Sens. 2010, 31, 1733–1752. [Google Scholar] [CrossRef]
- El Sobky, M.A.; Madani, A.A.; Surour, A.A. Spectral characterization of the Batuga granite pluton, South Eastern Desert, Egypt: Influence of lithological and mineralogical variation on ASD Terraspec data. Arab. J. Geosci. 2020, 13, 1246. [Google Scholar] [CrossRef]
- Chiari, R.; Longhi, I.; Sgavetti, M.A.; Gelli, A.; Orsi, A.; Pecoraro, F. Spectral classification of rocks: Analysis of laboratory 0.4- to 2.5-um reflectance and 2.5- to 25-um transmittance spectra of sedimentary and metamorphic rocks. In Proceedings of the Earth Surface Remote Sensing, London, UK, 22–25 September 1997; pp. 284–294. [Google Scholar]
- Wang, S.; Zhou, K.; Zhang, N.; Wang, J. Spectral data analysis of rock and mineral in Hatu Western Junggar Region, Xinjiang. In Proceedings of the SPIE Asia Pacific Remote Sensing, Beijing, China, 13–16 October 2014. [Google Scholar]
- Kumar, C.; Chatterjee, S.; Oommen, T.; Guha, A. Automated lithological mapping by integrating spectral enhancement techniques and machine learning algorithms using AVIRIS-NG hyperspectral data in Gold-bearing granite-greenstone rocks in Hutti, India. Int. J. Appl. Earth Obs. Geoinf. 2020, 86, 102006. [Google Scholar] [CrossRef]
- Tan, Y.; Lu, L.; Bruzzone, L.; Guan, R.; Chang, Z.; Yang, C. Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2020, 13, 471–486. [Google Scholar] [CrossRef]
- Murphy, R.J.; Monteiro, S.T.; Schneider, S. Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors. IEEE Trans. Geosci. Remote Sens. 2012, 50, 3066–3080. [Google Scholar] [CrossRef]
- Sgavetti, M.; Pompilio, L.; Meli, S. Reflectance spectroscopy (0.3–2.5 µm) at various scales for bulk-rock identification. Geosphere 2006, 2, 142–160. [Google Scholar] [CrossRef]
- Zhang, X.; Li, P. Lithological mapping from hyperspectral data by improved use of spectral angle mapper. Int. J. Appl. Earth Obs. Geoinf. 2014, 31, 95–109. [Google Scholar] [CrossRef]
- Mallat, S.G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell. 1989, 11, 674–693. [Google Scholar] [CrossRef]
- Yang, C.B.; Gao, W.B.; Hou, G.Y.; Li, X.Z.; Gao, M.T. Response Relationship between Feldspar Content and Characteristic Spectra in Igneous Rocks. Guang Pu Xue Yu Guang Pu Fen Xi/Spectrosc. Spectr. Anal. 2019, 39, 2077–2082. [Google Scholar] [CrossRef]
- Amer, R.; Kusky, T.; Ghulam, A. Lithological mapping in the Central Eastern Desert of Egypt using ASTER data. J. Afr. Earth Sci. 2010, 56, 75–82. [Google Scholar] [CrossRef]
- Hunt, G.R. Near-infrared (1.3–2.4 micrometre) spectra of alteration minerals—Potential for use in remote sensing. Geophysics 1979, 44, 1974–1986. [Google Scholar] [CrossRef]
- Guo, S.; Yang, C.; He, R.; Li, Y. Improvement of Lithological Mapping Using Discrete Wavelet Transformation from Sentinel-1 SAR Data. Remote Sens. 2022, 14, 5824. [Google Scholar] [CrossRef]
- Yang, C.B.; Liu, N.; Kuai, K.F. Research on Relationship between Spectral Characteristics, Physical Parameters and Metal Elements of Rocks in Xingcheng Area. Guang Pu Xue Yu Guang Pu Fen Xi/Spectrosc. Spectr. Anal. 2019, 39, 2953–2965. [Google Scholar] [CrossRef]
- Chen, B.; Feng, X.; Wu, R.; Guo, Q.; Wang, X.; Ge, S. Adaptive Wavelet Filter With Edge Compensation for Remote Sensing Image Denoising. IEEE Access 2019, 7, 91966–91979. [Google Scholar] [CrossRef]
- Lorenz, S.; Ghamisi, P.; Kirsch, M.; Jackisch, R.; Rasti, B.; Gloaguen, R. Feature extraction for hyperspectral mineral domain mapping: A test of conventional and innovative methods. Remote Sens. Environ. 2021, 252, 112129. [Google Scholar] [CrossRef]
- Feng, J.; Rogge, D.; Rivard, B. Comparison of lithological mapping results from airborne hyperspectral VNIR-SWIR, LWIR and combined data. Int. J. Appl. Earth Obs. Geoinf. 2018, 64, 340–353. [Google Scholar] [CrossRef]
- Mitchley, M.; Sears, M.; Damelin, S. Target detection in hyperspectral mineral data using wavelet analysis. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009; pp. IV-881–IV-884. [Google Scholar]
- Guo, B.; Guo, X.; Zhang, B.; Suo, L.; Bai, H.; Luo, P. Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine. Remote Sens. 2022, 14, 5804. [Google Scholar] [CrossRef]
- Pal, M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 2005, 26, 217–222. [Google Scholar] [CrossRef]
- Xi, Y.; Mohamed Taha, A.M.; Hu, A.; Liu, X. Accuracy comparison of various remote sensing data in lithological classification based on random forest algorithm. Geocarto Int. 2022, 37, 14451–14479. [Google Scholar] [CrossRef]
- Puletti, N.; Camarretta, N.; Corona, P. Evaluating EO1-Hyperion capability for mapping conifer and broadleaved forests. Eur. J. Remote Sens. 2016, 49, 157–169. [Google Scholar] [CrossRef]
- Zhang, X.; Xu, J.; Chen, Y.; Xu, K.; Wang, D. Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm. Sensors 2021, 21, 3395. [Google Scholar] [CrossRef] [PubMed]
- Rao, D.A.; Guha, A. Potential Utility of Spectral Angle Mapper and Spectral Information Divergence Methods for mapping lower Vindhyan Rocks and Their Accuracy Assessment with Respect to Conventional Lithological Map in Jharkhand, India. J. Indian Soc. Remote Sens. 2018, 46, 737–747. [Google Scholar] [CrossRef]
- Lu, Y.; Yang, C.; He, R. Towards lithology mapping in semi-arid areas using time-series Landsat-8 data. Ore Geol. Rev. 2022, 150, 105163. [Google Scholar] [CrossRef]
- Zhang, Y.; Wang, J.; Yu, J.; Tian, J.; Zhou, J. Study of Late Paleozoic Mineralization and Target Area Selection in the Jorotag Metallogenic Belt of the East Tianshan Mountains; Geological Survey Institute of Jilin University: Changchun, China, 2018. [Google Scholar]
- Daubechies, I. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 1988, 41, 909–996. [Google Scholar] [CrossRef]
- MATLAB, 9.14.0.2239454 (R2023a); The MathWorks Inc.: Natick, MA, USA, 2023.
- Misiti, Y.; Misiti, M.; Oppenheim, G.; Poggi, J.-M. Micronde: A Matlab Wavelet Toolbox for Signals and Images. In Wavelets and Statistics; Antoniadis, A., Oppenheim, G., Eds.; Springer: New York, NY, USA, 1995; pp. 239–259. [Google Scholar]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef]
- Liaw, A.; Wiener, M. Classification and regression by randomForest. R News 2002, 2, 18–22. [Google Scholar]
- Wong, T.T. Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognit. 2015, 48, 2839–2846. [Google Scholar] [CrossRef]
- Yokoya, N.; Miyamura, N.; Iwasaki, A. Detection and correction of spectral and spatial misregistrations for hyperspectral data. In Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010; pp. 1003–1006. [Google Scholar]
- Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145–163. [Google Scholar] [CrossRef]
- Pearson, K. Notes on the history of correlation. Biometrika 1920, 13, 25–45. [Google Scholar] [CrossRef]
- Rasouli Beirami, M.; Tangestani, M.H. A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions. Nat. Resour. Res. 2020, 29, 2949–2965. [Google Scholar] [CrossRef]
- Hecker, C.; Meijde, M.v.d.; Werff, H.v.d.; Meer, F.D. Assessing the Influence of Reference Spectra on Synthetic SAM Classification Results. IEEE Trans. Geosci. Remote Sens. 2008, 46, 4162–4172. [Google Scholar] [CrossRef]
- Lu, Y.; Yang, C.; Jiang, Q. Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units. Remote Sens. 2021, 13, 4824. [Google Scholar] [CrossRef]
- van der Meer, F.D.; van der Werff, H.M.; van Ruitenbeek, F.J.; Hecker, C.A.; Bakker, W.H.; Noomen, M.F.; van der Meijde, M.; Carranza, E.J.M.; de Smeth, J.B.; Woldai, T. Multi- and hyperspectral geologic remote sensing: A review. Int. J. Appl. Earth Obs. Geoinf. 2012, 14, 112–128. [Google Scholar] [CrossRef]
- Grebby, S.; Cunningham, D.; Tansey, K.; Naden, J. The impact of vegetation on lithological mapping using airborne multispectral data: A case study for the north Troodos region, Cyprus. Remote Sens. 2014, 6, 10860–10887. [Google Scholar] [CrossRef]
- Hewson, R.; Mshiu, E.; Hecker, C.; van der Werff, H.; van Ruitenbeek, F.; Alkema, D.; van der Meer, F. The application of day and night time ASTER satellite imagery for geothermal and mineral mapping in East Africa. Int. J. Appl. Earth Obs. Geoinf. 2020, 85, 101991. [Google Scholar] [CrossRef]
Name | Photo | Name | Photo |
---|---|---|---|
dolostone | andesite | ||
tuff | limestone | ||
sandstone | granite |
Parameters | GF-5 AHSI |
---|---|
Orbit altitude | 705 km |
Swath width | 60 km |
Spatial resolution | 30 m |
Spectral resolution | VNIR: 5 nm; SWIR: 10 nm |
Number of bands | VNIR: 150; SWIR: 180 |
Spectral range | 0.39–2.51 μm |
SWIR Signal-to-Noise Ratio (SNR) | ~500 |
Dispersive systems | Grating |
Decomposition–Reconstruction Method | Abbreviation (Using Haar Wavelets as an Example) |
---|---|
Reconstruct the high-frequency coefficients of the second and third levels after the three-level decomposition | haar_3_23 |
Reconstruct all three high-frequency coefficients after three-level decomposition | haar_3_123 |
Reconstruct the high-frequency coefficients of the second, third, and fourth levels after four-level decomposition | haar_4_234 |
Reconstruct all four high-frequency coefficients after four-level decomposition | haar_4_1234 |
Wavelength Range | Band Number | Wavelength |
---|---|---|
VNIR | VNIR 1–4 | 390–403 nm |
SWIR | SWIR 1–4 | 1004–1030 nm |
SWIR 43–50 | 1359–1418 nm | |
SWIR 96–112 | 1805–1940 nm |
Features | OA | Kappa Coefficients |
---|---|---|
haar_4_1234 | 0.599 | 0.502 |
haar_4_234 | 0.595 | 0.496 |
db2_4_234 | 0.593 | 0.493 |
haar_3_23 | 0.580 | 0.476 |
haar_3_123 | 0.574 | 0.467 |
db2_4_1234 | 0.572 | 0.465 |
db4_4_1234 | 0.556 | 0.441 |
db4_4_234 | 0.556 | 0.443 |
db2_3_23 | 0.551 | 0.435 |
db10_4_234 | 0.535 | 0.413 |
db2_3_123 | 0.530 | 0.405 |
db10_4_1234 | 0.526 | 0.403 |
db8_4_234 | 0.514 | 0.384 |
db8_4_1234 | 0.511 | 0.380 |
db4_3_23 | 0.489 | 0.346 |
db4_3_123 | 0.474 | 0.324 |
original spectrum | 0.465 | 0.335 |
db10_3_23 | 0.443 | 0.277 |
db10_3_123 | 0.432 | 0.262 |
db8_3_123 | 0.430 | 0.260 |
db8_3_23 | 0.428 | 0.257 |
Rock Types | Highest F1-Score | Features (Highest F1-Score) | F1-Score of Haar_4_1234 | Haar_4_1234 Ranking | Original F1-Score | Original F1-Score Ranking |
---|---|---|---|---|---|---|
Dolostone | 0.689 | db2_4_234 | 0.655 | 7 | 0.464 | 18 |
Andesite | 0.418 | db2_4_234 | 0.383 | 2 | 0.271 | 9 |
Tuff | 0.519 | db2_3_123 | 0.462 | 4 | 0.398 | 18 |
Limestone | 0.665 | Haar_4_1234 | 0.665 | 1 | 0.457 | 21 |
Sandstone | 0.465 | Haar_4_234 | 0.456 | 2 | 0.270 | 8 |
Granite | 0.743 | Haar_4_234 | 0.740 | 2 | 0.643 | 16 |
Study Area A | Study Area B | ||||
---|---|---|---|---|---|
Features | OA | Kappa Coefficients | Features | OA | Kappa Coefficients |
haar_4_1234 | 0.769 | 0.736 | haar_4_1234 | 0.885 | 0.862 |
haar_4_234 | 0.768 | 0.735 | haar_3_123 | 0.884 | 0.861 |
haar_3_123 | 0.766 | 0.733 | haar_4_234 | 0.880 | 0.856 |
haar_3_23 | 0.760 | 0.725 | haar_3_23 | 0.878 | 0.853 |
db4_4_234 | 0.721 | 0.681 | db2_4_234 | 0.870 | 0.844 |
db2_3_123 | 0.720 | 0.68 | db2_4_1234 | 0.868 | 0.842 |
db2_4_1234 | 0.720 | 0.68 | db10_4_1234 | 0.858 | 0.829 |
db2_4_234 | 0.718 | 0.677 | db2_3_123 | 0.857 | 0.828 |
db2_3_23 | 0.717 | 0.676 | db2_3_23 | 0.854 | 0.825 |
db10_4_1234 | 0.709 | 0.668 | db8_4_1234 | 0.853 | 0.823 |
db10_4_234 | 0.709 | 0.667 | db4_3_123 | 0.849 | 0.819 |
db4_4_1234 | 0.709 | 0.667 | db10_4_234 | 0.849 | 0.819 |
db8_4_1234 | 0.700 | 0.658 | db8_4_234 | 0.849 | 0.819 |
db4_3_23 | 0.700 | 0.657 | db4_4_1234 | 0.849 | 0.819 |
db8_4_234 | 0.699 | 0.656 | db4_3_23 | 0.848 | 0.818 |
db4_3_123 | 0.698 | 0.655 | db4_4_234 | 0.848 | 0.817 |
db8_3_123 | 0.686 | 0.641 | db8_3_123 | 0.839 | 0.807 |
db8_3_23 | 0.685 | 0.64 | db10_3_123 | 0.836 | 0.803 |
original | 0.683 | 0.637 | db8_3_23 | 0.833 | 0.799 |
db10_3_123 | 0.679 | 0.633 | db10_3_23 | 0.833 | 0.799 |
db10_3_23 | 0.662 | 0.614 | original | 0.813 | 0.776 |
Rock Units | Highest F1-Score | Features (Highest F1-Score) | F1-Score of Haar_4_1234 | Haar_4_1234 Ranking | Original F1-Score | Original F1-Score Ranking |
---|---|---|---|---|---|---|
C1x | 0.745 | haar_4_234 | 0.737 | 2 | 0.627 | 21 |
C1gd | 0.720 | haar_3_123 | 0.679 | 3 | 0.643 | 7 |
C1y | 0.802 | haar_3_123 | 0.795 | 3 | 0.665 | 21 |
Pξγ | 0.907 | haar_3_123 | 0.905 | 4 | 0.874 | 15 |
PQg | 0.786 | haar_4_1234 | 0.786 | 1 | 0.706 | 19 |
P1aer | 0.651 | haar_4_1234 | 0.651 | 1 | 0.555 | 20 |
Pγδ | 0.813 | haar_4_1234 | 0.813 | 1 | 0.721 | 13 |
Pγδπ | 0.776 | haar_4_1234 | 0.776 | 1 | 0.650 | 16 |
Rock Units | Highest F1-Score | Features (Highest F1-Score) | F1-Score of Haar_4_1234 | Haar_4_1234 Ranking | Original F1-Score | Original F1-Score Ranking |
---|---|---|---|---|---|---|
(E3-N1)t | 0.875 | haar_4_1234 | 0.875 | 1 | 0.796 | 21 |
Qp2W | 0.866 | haar_4_1234 | 0.866 | 1 | 0.780 | 11 |
Qp3X | 0.881 | haar_3_123 | 0.863 | 3 | 0.780 | 17 |
Cγ | 0.886 | db2_4_234 | 0.876 | 5 | 0.788 | 21 |
Cξγ | 0.907 | db8_4_1234 | 0.896 | 6 | 0.864 | 21 |
C2wt | 0.933 | haar_4_1234 | 0.933 | 1 | 0.872 | 21 |
Research Target | Pearson Correlation Coefficient |
---|---|
Laboratory spectra | −0.734 |
Image spectra (study area A) | −0.588 |
Image spectra (study area B) | −0.751 |
Laboratory Spectra | Study Area A | Study Area B | |||
---|---|---|---|---|---|
Rock | Pearson Correlation Coefficient | Rock Unit | Pearson Correlation Coefficient | Rock Unit | Pearson Correlation Coefficient |
Dolostone | −0.971 | C1x | −0.849 | (E3-N1)t | −0.600 |
Andesite | −0.202 | C1gd | 0.197 | Qp2W | −0.183 |
Tuff | −0.857 | C1y | −0.610 | Qp3X | −0.583 |
Limestone | −0.818 | Pξγ | −0.638 | Cγ | −0.973 |
Sandstone | 0.101 | PQg | −0.600 | Cξγ | −0.827 |
Granite | −0.535 | P1aer | −0.700 | C2wt | −0.889 |
Pγδ | −0.163 | ||||
Pγδπ | −0.611 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Guo, S.; Jiang, Q. Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data. Remote Sens. 2023, 15, 5334. https://doi.org/10.3390/rs15225334
Guo S, Jiang Q. Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data. Remote Sensing. 2023; 15(22):5334. https://doi.org/10.3390/rs15225334
Chicago/Turabian StyleGuo, Senmiao, and Qigang Jiang. 2023. "Improving Rock Classification with 1D Discrete Wavelet Transform Based on Laboratory Reflectance Spectra and Gaofen-5 Hyperspectral Data" Remote Sensing 15, no. 22: 5334. https://doi.org/10.3390/rs15225334