Using the HySpex hyperspectral imaging system for mapping the alteration zones in the Yudai, Kalatage district, NW China
Article Sidebar
Main Article Content
摘要
The HySpex hyperspectral data used in this study have a wide spectral response range, narrow bandwidth, and high spatial resolution, and they can be effectively applied to the extraction of mineral alteration information. We explore how to extract effective information from remote sensing images through remote sensing image classification technology and explore its utilization in geological science. This study aims to verify the reliability and accuracy of alteration information extracted by using a super-low altitude detection platform equipped with powered delta wings mounted with HySpex hyperspectral sensors in the Yudai area of the Kalatage district. Field data were collected and analyzed using Analytical Spectral Devices (ASD), and the results were compared with those obtained from the United States Geological Survey (USGS) spectral library. The analysis of the geological background and HySpex hyperspectral data was enhanced by Minimum Noise Fraction (MNF) transformation coupled with the Pixel Purity Index (PPI) to extract endmembers of altered minerals, including chlorite and jarosite, from different spectra (SWIR and VNIR) and spectral wavelengths. Additionally, two classification methods, the Spectral Angle Mapper (SAM) and Support Vector Machine (SVM), were applied to the data for effective mineral mapping. The best-performing method, i.e., SVM, was validated using ground-truth information obtained during field observations. The results from the classification methods revealed accuracies of 59.57% for SAM and 69.25% for SVM. The HySpex hyperspectral data obtained using a super-low altitude detection platform proved highly effective for detecting altered rock information. Thus, this approach has great potential for the rapid identification of geological and mineralogical features, especially in complex terrains.
Article Details
本作品采用知识共享署名 4.0 国际许可协议授权。
参考文献
Adão, T.; Hruška, J.; Pádua, L.; Bessa, J.; Peres, E.; Morais, R.; Sousa, J.J. Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing 2017, 9, 1110.
Baissa, R.; Labbassi, K.; Launeau, P.; Gaudin, A.; Ouajhain, B. Using HySpex SWIR-320m hyperspectral data for the identification and mapping of minerals in hand specimens of carbonate rocks from the Ankloute Formation (Agadir Basin, Western Morocco). Journal of African Earth Sciences 2011, 61, 1-9.
Bedini, E. The use of hyperspectral remote sensing for mineral exploration: A review. Journal of Hyperspectral Remote Sensing 2017, 7, 189-211.
Bishop, C.A.; Liu, J.G.; Mason, P.J. Hyperspectral remote sensing for mineral exploration in Pulang, Yunnan Province, China. International Journal of Remote Sensing 2011, 32, 2409-2426.
Bishop, J.L.; Murad, E. The visible and infrared spectral properties of jarosite and alunite. American Mineralogist 2005, 90, 1100-1107.
Boardman, J.; Kruse, F. Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada: in Proceedings, ERIM Tenth Thematic Conference on Geologic Remote Sensing. Environmental Research Institute of Michigan, Ann Arbor, MI, pp. I-407-I-418 1994.
Boardman, J.W. Automating spectral unmixing of AVIRIS data using convex geometry concepts. 1993.
Boser, B.; Guyon, I.; Vapnik, V. A training algorithm for optimal margin classifiers. Proceedings of the fifth annual workshop on Computational learning theory; Pittsburgh, Pennsylvania, USA. 130401: ACM. 1992.
Chehdi, K.; Soltani, M.; Cariou, C. Pixel classification of large-size hyperspectral images by affinity propagation. Journal of applied remote sensing 2014, 8, 083567.
Chen, D.; Yang, D.; Zhang, X.; Zhao, Y.; Zhang, Y. Spatial Suitability Evaluation of an Arid City Based on the Perspective of Major Function Oriented Zoning: A Case Study of Urumqi City in Xinjiang, China. Sustainability 2018, 10, 3004.
Chen, L.; Wang, J.-B.; Bagas, L.; Wu, X.-B.; Zou, H.-Y.; Zhang, H.-Q.; Sun, Y.; Lv, X.-Q.; Deng, X.-H. Significance of adakites in petrogenesis of Early Silurian magmatism at the Yudai copper deposit in the Kalatag district, NW China. Ore Geology Reviews 2017, 91, 780-794.
Clark, R.N. Spectroscopy of rocks and minerals, and principles of spectroscopy. Manual of remote sensing 1999, 3, 2-2.
Cortes, C.; Vapnik, V. Support-vector networks. Machine learning 1995, 20, 273-297.
Crestani, F.; Lalmas, M.; Van Rijsbergen, C.J.; Campbell, I. “Is this document relevant?… probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR) 1998, 30, 528-552.
Crowley, J.; Williams, D.; Hammarstrom, J.; Piatak, N.; Chou, I.-M.; Mars, J. Spectral reflectance properties (0.4–2.5 μm) of secondary Fe-oxide, Fe-hydroxide, and Fe-sulphate-hydrate minerals associated with sulphide-bearing mine wastes. Geochemistry: Exploration, Environment, Analysis 2003, 3, 219-228.
Deng, X.-H.; Wang, J.-B.; Pirajno, F.; Wang, Y.-W.; Li, Y.-C.; Li, C.; Zhou, L.-M.; Chen, Y.-J. Re–Os dating of chalcopyrite from selected mineral deposits in the Kalatag district in the eastern Tianshan Orogen, China. Ore Geology Reviews 2016, 77, 72-81.
Deng, X.-H.; Wang, J.-B.; Santosh, M.; Li, Y.-C.; Wang, Y.-W.; Mao, Q.-G.; Long, L.-L.; Chen, X. New 40Ar/39Ar ages from the Kalatag district in the Eastern Tianshan, NW China: Constraints on the timing of Cu mineralization and stratigraphy. Ore Geology Reviews 2018, 100, 250-262.
Deng, X.H.; Wang, J.B.; Santosh, M.; Wang, Y.W.; Long, L.L.; Zhang, H.Q.; Yang, L.Y.; Xu, J.; Chen, X.; Chen, L. Early Paleozoic volcanic rocks with VMS mineralization from eastern Tianshan Orogen: Implication for tectonic evolution. Geological Journal 2018, 53, 2178-2192.
Denk, M.; Gläßer, C.; Kurz, T.H.; Buckley, S.J.; Drissen, P. Mapping of iron and steelwork by-products using close range hyperspectral imaging: A case study in Thuringia, Germany. European Journal of Remote Sensing 2015, 48, 489-509.
Erickson, R.L. Characteristics of mineral deposit occurrences; The Survey: 1982.
Farag, A.A.; Mohamed, R.M. Classification of multispectral data using support vector machines approach for density estimation. In Proceedings of International Conference on Intelligent Engineering System; pp. 6-8.
FILHO, A.P.C.S.A.C.R.D.S. HYPERSPECTRAL REMOTE SENSING FOR MINERAL MAPPING: A CASE-STUDYAT ALTO PARAÍSO DE GOÍAS, CENTRAL BRAZIL. 2000.
Foody, G.M.; Mathur, A. Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sensing of Environment 2004, 93, 107-117.
Fricke, K. Analysis and modelling of water supply and demand under climate change, land use transformation and socio-economic development: The water resource challenge and adaptation measures for Urumqi Region, Northwest China; Springer Science & Business Media: 2013.
Gualtieri, J.A. The support vector machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data. Kernel methods for remote sensing data analysis 2009, 3, 51-83.
Guo, J.; Zhou, K.; Wang, J.; Cui, S.; Zhou, S.; Tang, C. Identification of iron-bearing minerals based on HySpex hyperspectral remote sensing data. Journal of Applied Remote Sensing 2019, 13, 047501.
Huang, C.; Davis, L.; Townshend, J. An assessment of support vector machines for land cover classification. International Journal of remote sensing 2002, 23, 725-749.
Hudson, W.D. Correct formulation of the kappa coefficient of agreement. Photogrammetric engineering and remote sensing 1987, 53, 421-422.
Koerting, F.; Rogass, C.; Kaempf, H.; Lubitz, C.; Harms, U.; Schudack, M.; Kokaly, R.; Mielke, C.; Boesche, N.; Altenberger, U. DRILL CORE MINERAL ANALYSIS BY MEANS OF THE HYPERSPECTRAL IMAGING SPECTROMETER HySpex, XRD AND ASD IN PROXIMITY OF THE MÝTINA MAAR, CZECH REPUBLIC. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences 2015, 40.
Kruse, F.; Lefkoff, A.; Dietz, J. Expert system-based mineral mapping in northern Death Valley, California/Nevada, using the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment 1993, 44, 309-336.
Lenhard, K.; Baumgartner, A.; Schwarzmaier, T. Independent laboratory characterization of NEO HySpex imaging spectrometers VNIR-1600 and SWIR-320m-e. IEEE Transactions on Geoscience and Remote Sensing 2014, 53, 1828-1841.
Lennon, M.; Mercier, G.; Hubert-Moy, L. Classification of hyperspectral images with nonlinear filtering and support vector machines. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium; pp. 1670-1672.
Mao, Q. The geological, metallogenesis and metallogenic prognosis studies of the Kalatage copper polymetallic ore district in eastern Tianshan, NW China. Post-Doctoral Research Report 2014, 1-154.
Mathieu, M. Alteration mapping on drill cores using a HySpex SWIR-320m hyperspectral camera: Application to the exploration of an unconformity-related uranium deposit (Saskatchewan, Canada). 2017.
Melgani, F.; Bruzzone, L. Support vector machines for classification of hyperspectral remote-sensing images. In Proceedings of IEEE international geoscience and remote sensing symposium; pp. 506-508.
Molan, Y.E.; Refahi, D.; Tarashti, A.H. Mineral mapping in the Maherabad area, eastern Iran, using the HyMap remote sensing data. International Journal of Applied Earth Observation and Geoinformation 2014, 27, 117-127.
Murphy, R.J.; Monteiro, S.T.; Schneider, S. Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors. IEEE Transactions on Geoscience and Remote Sensing 2012, 50, 3066-3080.
Murphy, R.J.; Taylor, Z.; Schneider, S.; Nieto, J. Mapping clay minerals in an open-pit mine using hyperspectral and LiDAR data. European Journal of Remote Sensing 2017, 48, 511-526, doi:10.5721/EuJRS20154829.
Pour, A.B.; Hashim, M. Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, SE Iran. Journal of Asian Earth Sciences 2011, 42, 1309-1323.
Salehi, S.; Thaarup, S.M. Mineral mapping by hyperspectral remote sensing in West Greenland using airborne, ship-based and terrestrial platforms. Geological Survey of Denmark and Greenland Bulletin 2017, 47-50.
Schwarz, J.; Staenz, K. Adaptive threshold for spectral matching of hyperspectral data. Canadian Journal of Remote Sensing 2001, 27, 216-224.
Vincent, R.K. Fundamentals of geological and environmental remote sensing; Prentice Hall Upper Saddle River, NJ: 1997; Vol. 366.
Xiao, W.; Mao, Q.; Windley, B.; Han, C.; Qu, J.; Zhang, J.; Ao, S.; Guo, Q.; Cleven, N.; Lin, S. Paleozoic multiple accretionary and collisional processes of the Beishan orogenic collage. American Journal of Science 2010, 310, 1553-1594.
Xiao, W.-J.; Zhang, L.-C.; Qin, K.-Z.; Sun, S.; Li, J.-L. Paleozoic accretionary and collisional tectonics of the Eastern Tianshan (China): implications for the continental growth of central Asia. American Journal of Science 2004, 304, 370-395.