Optics and Precision Engineering, Volume. 23, Issue 8, 2407(2015)

Research and development of mineral identification utilizing hyperspectral remote sensing

ZHANG Cheng-ye*... QIN Qi-ming, Chen Li, Wang Nan and Zhao Shan-shan |Show fewer author(s)
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    References(76)

    [1] [1] GOETZ A F H. Spectroscopic remote-sensing for geological applications [C]. Proceedings of the Society of Photo-Optical Instrumentation Engineers, Los Angeles, USA: SPIE, 1981(268): 17-21.

    [2] [2] GOETZ A. Imaging spectrometry for Earth remote sensing [J]. Science, 1985, 228(4704): 1147-1153.

    [3] [3] MAGENDRAN T, SANJEEVI S. Hyperion image analysis and linear spectral unmixing to evaluate the grades of iron ores in parts of Noamundi, Eastern India [J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 26: 413-426.

    [4] [4] TONG Q X, ZHANG B, ZHENG L F. Hyperspectral Remote Sensing [M]. Beijing: Higher Education Press, 2006. (in Chinese)

    [8] [8] MULDER V L, DE BRUIN S, WEYERMANN J, et al.. Characterizing regional soil mineral composition using spectroscopy and geostatistics [J]. Remote Sensing of Environment, 2013, 139: 415-429.

    [9] [9] VAN DER MEER F D, VAN DER WERFF H M A, VAN RUITENBEEK F J A, et al.. Multi- and hyperspectral geologic remote sensing: A review [J]. International Journal of Applied Earth Observation and Geoinformation, 2012, 14(1): 112-128.

    [10] [10] TIAN F. Identification and quantitative retrival of minerals information integrating VIS-NIR-MIR-TIR (0.35~25μm) hyperspectral data [D]. Beijing: China University of Geoscience (Beijing), 2010. (in Chinese)

    [11] [11] HUNT G R. Spectral signatures of particulate minerals in visible and near IR [J]. Geophysics, 1977, 42(3): 501-513.

    [12] [12] HUNT G R. Near-infrared (1.3-2.4 Mu-M) spectra of alteration minerals-potential for use in remote-sensing [J]. Geophysics, 1979, 44(12): 1974-1986.

    [13] [13] CLARK R N. Spectral properties of mixture of montmorillonite and dark carbon grains: Implications for remote sensing minerals containing chemically and physically absorbed water [J]. Journal of Geophysical Research, 1983, 88: 10635-10644.

    [14] [14] GAFFEY S J. Spectral reflectance of carbonate minerals in the visible and near infrared (0.35-2.55 microns): anhydrous carbonate minerals [J]. Journal of Geophysical Research, 1987, 92: 1429-1440.

    [15] [15] LI X W, LIU S H. Principle and Application of Remote Sensing [M]. Beijing: Science Press, 2008: 231. (in Chinese)

    [16] [16] CLARK R N, KING T V V, KLEJWA M, et al.. High spectral resolution reflectance spectroscopy of minerals [J]. Journal of Geophysical Research-Solid Earth and Planets, 1990, 95(B8): 12653-12680.

    [17] [17] ZHU ZH H, WANG W Y, PENG X L. Study on the direct detection of hydrocarbon microleakage using remote sensing [J]. Chinese Science Bulletin, 1990, 16: 1257-1260. (in Chinese)

    [18] [18] CHANG Q. Preliminary study on spectrum features of rocks (ores) from southern belt of beishan area in gansu province [J]. Acta Geologica Gansu, 1999, 8(1): 49-56. (in Chinese)

    [19] [19] WU Y ZH, TIAN Q J, CHEN J, et al.. Application of rock laboratorial reflectance spectra in Hami area based on principal component analysis [J]. Acta Petrologica Sinica, 2003, 19(4): 761-766. (in Chinese)

    [20] [20] CHEN M. Study on the spectral model of rocks and minerals based on fractal [D]. Wuhan: Huazhong University of Science and Technology, 2010. (in Chinese)

    [21] [21] PENG J, LI X, ZHOU Q, et al.. Influence of iron oxide on the spectral characteristics of organic matter [J]. Journal of Remote Sensing, 2013, 17(6): 1396-1412. (in Chinese)

    [23] [23] MURPHY R J, SCHNEIDER S, MONTEIRO S T. Consistency of measurements of wavelength position from hyperspectral imagery: use of the ferric iron crystal field absorption at similar to 900 nm as an indicator of mineralogy[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52: 2843-2857.

    [24] [24] ZHOU Z Y. Progress in hyperspectral remote sensing for petroleum prospecting [J]. Remote Sensing Technology and Application, 2014, 29(2): 352-361. (in Chinese)

    [25] [25] KRUSE F A, TARANIK J V, COOLBAUGH M, et al.. Effect of reduced spatial resolution on mineral mapping using imaging spectrometry-examples using hyperspectral infrared imager (hyspiri)-simulated data [J]. Remote Sensing, 2011, 3(8): 1584-1602.

    [26] [26] MIELKE C, BOESCHE N K, ROGASS C, et al.. Spaceborne mine waste mineralogy monitoring in south africa, applications for modern push-broom missions: Hyperion/OLI and EnMAP/Sentinel-2 [J]. Remote Sensing, 2014, 6(8): 6790-6816.

    [27] [27] ROGGE D, RIVARD B, SEGL K, et al.. Mapping of NiCu-PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada [J]. Remote Sensing of Environment, 2014, 152: 302-17.

    [28] [28] EO-1 User Guide Version 2.3, USGS Earth Resources Observation System Data Centre EDC [EB], 2003.7.15.

    [30] [30] ROBERTS D A, QUATTROCHI D A, HULLEY G C, et al.. Synergies between VSWIR and TIR data for the urban environment: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) Decadal Survey mission [J]. Remote Sensing of Environment, 2012, 117(0): 83-101.

    [31] [31] BERGERON M, HOLLINGER A, STAENZ K, et al.. Hyperspectral Environment and Resource Observer (HERO) mission [J]. Canadian Journal of Remote Sensing, 2008, 341: S1-S11.

    [32] [32] GALEAZZI C, SACCHETTI A, CISBANI A, et al.. The PRISMA program [C]. 2008 IEEE International Geoscience and Remote Sensing Symposium, Boston, MA, USA: IGARSS, 2008, 4(IV): 105-108.

    [33] [33] LABATE D, CECCHERINI M, CISBANI A, et al.. The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission [J]. Acta Astronautica, 2009, 65(9-10): 1429-1436.

    [34] [34] KRUSE F A. Preliminary results-hyperspectral mapping of coral reef systems using EO-1 Hyperion Buck Island and U.S Virgin Islands [C]. Proceedings of the 12th JPL Airborne Geoscience Workshop, Pasadena, California, USA: JPL Publication, 2003, 04-6: 157-173.

    [35] [35] CLARK R N, SWAYZE G A S, LIVO K E, et al.. Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and Expert Systems [J]. Journal of Geophysical Research, 2003, 108 (E12): 5131.

    [36] [36] GAN F P, WANG R SH. Research of Information Extraction Foundation and Technical Methods for Remote Sensing Information [M]. Beijing: Geological Publishing House, 2004. (in Chinese)

    [37] [37] VAN DER MEER F D. Analysis of spectral absorption features in hyperspectral imagery [J]. International Journal of Applied Earth Observation and Geoinformation, 2004, 5: 55-68.

    [38] [38] GAN F P, WANG R SH, MA A N, et al.. The development and tendency of both basis and techniques of discrimination for minerals and rocks using spectral remote sensing data [J]. Remote Sensing Technology and Application, 2002, 17(3): 140-147. (in Chinese)

    [39] [39] CROWLEY J K, BRICKEY D W, ROWAN L C. Airborne imaging spectrometer data of the Ruby Mountains, Montana - mineral discrimination using relative absorption band-depth images [J]. Remote Sensing of Environment, 1989, 29(2): 121-134.

    [40] [40] WANG Y. Hydrocarbon microseepage information extracting through remote sensing technology in front range of longmenshan [J]. Coal Geology of China, 2010, 22(10): 10-16. (in Chinese)

    [41] [41] YANG Y J, ZHAO Y J. The hyperspectral research status at home and abroad in oil exploration [J]. Science Technology and Engineering, 2011, 11(6): 1290-1299. (in Chinese)

    [42] [42] CLARK R N, GALLAGHER A J, SWAYZE G A. Material absorption band depth mapping of imaging spectrometer data using the complete band shape least-squares algorithm simultaneously fit to multiple spectral features from multiple materials [C]. Proceedings of the Third Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Workshop, JPL Publication, 1990, 90-94: 176 -186.

    [43] [43] 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 [J]. Remote Sensing of Environment, 2007, 110(3): 344-356.

    [45] [45] ZHANG Z G, WANG R SH, GUO X F, et al.. Mineral recognition method by spectrometry remote sensing based on material spectral characteristics [J]. Earth Science Frontiers, 2003,10(2): 437-443. (in Chinese)

    [46] [46] GAN F P, WANG R SH, MA A N. Spectral identification tree(sit) for mineral extraction based on spectral characteristics of minerals [J]. Earth Science Frontiers, 2003,10(2): 445-454. (in Chinese)

    [47] [47] CHE Y F, ZHAO Y J, YI P Y, et al.. Hyperspectral remote sensing mineral information extraction based on the spectral primary and secondary absorption bands combination features of spectral similarity measure[J]. Science Technology and Engineering, 2014, 14(34): 1-5. (in Chinese)

    [49] [49] FENSTERMAKER L K, MILLER J R. Identification of fluvially redistributed mill tailings using high-spectral-resolution aircraft data [J]. Photogrammetric Engineering and Remote Sensing, 1994, 60(8): 989-995.

    [50] [50] YAN SH X, ZHANG B, ZHAO Y CH,et al.. Summarizing the technical flow and main approaches for discrimination and mapping of rocks and minerals using hyperspectral remote sensing [J]. Remote Sensing Technology and Application, 2004, 19(1): 52-63. (in Chinese)

    [51] [51] LIU H H, YANG W N, YANG R H. A comparative study on the mineral identification methods using hyperspectral remote sensing data [J]. Geology and Exploration, 2013,49(2): 359-366. (in Chinese)

    [52] [52] KRUSE F A, LEFKOFF A B, DIETZ J B. Expert system-based mineral mapping in northern death-valley, californianevada, using the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) [J]. Remote Sensing of Environment, 1993, 44: 309-336.

    [54] [54] ZHANG X Y, LI P J. Litho logical mapping from hyperspectral data by improved use of spectral angle mapper [J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 31: 95-109.

    [55] [55] MOLAN Y E, REFAHI D, TARASHTI A H. Mineral mapping in the Maherabad area, eastern Iran, using the HyMap remote sensing data [J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 27(B): 117-127.

    [56] [56] 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 [J]. International Journal of Remote Sensing, 2010, 31(7): 1733-52.

    [57] [57] MOUNTRAKIS G, IM J, OGOLE C. Support vector machines in remote sensing: A review [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(3): 247-259.

    [58] [58] ZHANG B, SUN X, GAO L R, et al.. Endmember extraction of hyperspectral remote sensing images based on the Ant Colony Optimization (ACO) algorithm [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(7): 2635-2646.

    [59] [59] ZHANG B, SUN X, GAO L R, et al.. Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(11): 4173-4176.

    [60] [60] ZHANG B, GAO J W, GAO L R, et al.. Improvements in the ant colony optimization algorithm for endmember extraction from hyperspectral images [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013,6(2SI2): 522-530.

    [61] [61] ZHAO X M. Study on using hyperspectral remote sensing to explore oil & gas resources based on hydrocarbon microseepage theory [D]. Beijing: China University of Geoscience, 2007. (in Chinese)

    [62] [62] NEVILLE R A, LEVESQUE J, STAENZ K, et al.. Spectral unmixing of hyperspectral imagery for mineral exploration: comparison of results from SFSI and AVIRIS [J]. Canadian Journal of Remote Sensing, 2003, 29(1): 99-110.

    [63] [63] ROGGE D M, RIVARD B, ZHANG J K, et al.. Iterative spectral unmixing for optimizing per-pixel endmember sets [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3725-3736.

    [64] [64] HEYLEN R, BURAZEROVIC D, SCHEUNDERS P, Fully constrained least squares spectral unmixing by simplex projection [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11SI1): 4112-4122.

    [65] [65] LIU K, ZHANG L F, QIN H H. Weighted spectral unmixing method for hyperspectral mineral mapping [J]. Journal of Remote Sensing, 2013, 17( 3): 609-625. (in Chinese)

    [66] [66] ZHANG B, ZHUANG L N, GAO L R, et al.. PSO-EM: a hyperspectral unmixing algorithm based on normal compositional model [J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(12): 7782-7792.

    [67] [67] POULET F, ERARD S. Nonlinear spectral mixing: Quantitative analysis of laboratory mineral mixtures [J]. Journal of Geophysical Research-Planets, 2004, 109(E0): E02009.

    [69] [69] ALTMANN Y, DOBIGEON N, TOURNERET J Y. Nonlinearity detection in hyperspectral images using a polynomial post-nonlinear mixing model [J]. IEEE Transactions on Image Processing, 2013, 22(4): 1267-1276.

    [70] [70] HEYLEN R, PARENTE M, GADER P. A review of nonlinear hyperspectral unmixing methods [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6SI): 1844-1868.

    [71] [71] CALLICO G M, LOPEZ S, AGUILAR B, et al.. Parallel implementation of the modified vertex component analysis algorithm for hyperspectral unmixing using OpenCL [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3650-3659.

    [72] [72] BERNABE S, SANCHEZ S, PLAZA A, et al.. Hyperspectral unmixing on GPUs and multi-core processors: a comparison [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3SI): 1386-1398.

    [73] [73] GONZALEZ C, RESANO J, PLAZA A, et al.. FPGA implementation of abundance estimation for spectral unmixing of hyperspectral data using the image space reconstruction algorithm[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012,5(1SI): 248-261.

    [74] [74] SANCHEZ S, PAZ A, MARTIN G, et al.. Parallel unmixing of remotely sensed hyperspectral images on commodity graphics processing units [J]. Concurrency and Computation-Practice & Experience, 2011, 23(13): 1538-1557.

    [75] [75] CHEN SH B, LIU Y L, YANG Q, et al.. Lithologic classification from hyperspectral data in dense vegetation cover area [J]. Journal of Jilin University: Earth Science Edition, 2012(6): 1959-1965. (in Chinese)

    [76] [76] LIU Y L. Study on hyperspectral remote sensing methods for rock classification and mineral identification in vegetation covered area [D]. Changchun: Jilin University, 2013. (in Chinese)

    [77] [77] GONG P. Some forefront problems on remote sensing science and technology [J]. Journal of Remote Sensing, 2009, 13(1): 13-23. (in Chinese)

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    ZHANG Cheng-ye, QIN Qi-ming, Chen Li, Wang Nan, Zhao Shan-shan. Research and development of mineral identification utilizing hyperspectral remote sensing[J]. Optics and Precision Engineering, 2015, 23(8): 2407

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    Paper Information

    Received: Apr. 10, 2015

    Accepted: --

    Published Online: Oct. 22, 2015

    The Author Email: Cheng-ye ZHANG (zhangchengye@pku.edu.cn)

    DOI:10.3788/ope.20152308.2407

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