Lithological fuzzy classification by combining WorldView-2 data and OLI data

Shuang SHUAI; Zhi ZHANG; Xinbiao LYU; Zicheng MA; Si CHEN; Lina HAO

doi:10.11834/jrs.20210434

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Lithological fuzzy classification by combining WorldView-2 data and OLI data
Vol. 26, Issue 6, Pages: 1247-1259(2022)
Received：30 September 2020，

Published：07 June 2022
DOI： 10.11834/jrs.20210434
稿件说明：

移动端阅览

帅爽，张志，吕新彪，马梓程，陈思，郝利娜.2022.WorldView-2影像与OLI影像协同岩性模糊分类.遥感学报，26（6）： 1247-1259 DOI： 10.11834/jrs.20210434.

Shuai S，Zhang Z，Lyu X B，Ma Z C，Chen S and Hao L N. 2022. Lithological fuzzy classification by combining WorldView-2 data and OLI data. National Remote Sensing Bulletin， 26‍（6）：1247-1259 DOI： 10.11834/jrs.20210434.

摘要

Landsat系列、ASTER等中等空间分辨率遥感数据（中分数据）覆盖了碳酸盐矿物、粘土矿物、铁氧化物矿物等矿物的诊断光谱区间，广泛应用于矿物、岩石信息提取，但受限于空间分辨率，混合像元现象明显，严重制约了其岩性分类精度。WorldView-2、QuickBird等高空间分辨率遥感数据（高分数据）提供了岩石地层表面丰富的空间结构信息，同时空间分辨率的提高也是缓解混合像元效应的最有效途径，但高分数据覆盖的光谱区间往往较窄，难以满足大多数特征吸收谱段位于短波红外、热红外区间的矿物、岩石信息提取。在岩性自动分类方法上，前人研究中仍以采用基于像元的分类方法为主，分类结果的“椒盐现象（Salt-and-pepper，出现在分类结果图中大量孤立的错分点或小图斑）”严重。为结合中分数据的光谱信息优势和高分数据的空间结构信息优势，同时减少基于像元的岩性分类方法中的“椒盐现象”，提高岩性自动分类精度，本文以Landsat 8 OLI数据和WorldView-2数据为例，提出了一种协同中、高分遥感数据进行面向对象的岩性模糊分类的方法。首先通过“结构协同”和“光谱协同”方案对WorldView-2数据和OLI数据进行信息协同，利用主成分变换对协同后数据的纹理信息和光谱信息进行压缩和增强，然后将增强后的纹理信息和光谱信息进行波段绑定，并进行多尺度分割。根据岩性单元间的光谱特征和纹理特征的差异，构建各岩性单元的模糊逻辑隶属度函数，实现对研究区岩性的模糊分类。实验结果表明，该方法成功划分了岩性单元的分布，总体岩性分类精度为89.35％。

Abstract

Medium spatial resolution data

such as TM (Theme Mapper)

ETM+ (Enhanced Theme Mapper Plus)

OLI (Operational Land Imager) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

has been widely applied in lithological mapping and minerals mapping

because of covering the diagnostic spectral regions of carbonate rocks

clay minerals

iron oxide minerals

etc. However

due to the relatively coarse spatial resolution

the phenomenon of mixed pixels is obvious

which severely restricts the accuracy of lithological mapping of medium spatial resolution data. High spatial resolution data

such as WorldView and QuickBird

provides rich spatial structure information of rock surfaces. Meanwhile

the improvement of spatial resolution is also the most effective way to alleviate the phenomenon of mixed pixels

but the spectral range of high spatial resolution data is often narrow and difficult to extract of minerals and rocks with characteristic reflectance absorption in short-wave infrared and thermal infrared regions. And

for lithological classification method

pixel-based classification methods are still mainly used in previous studies

exhibiting the undesired “salt-and-pepper” phenomenon.

The objectives of this study are to (1) combine and enhance the spectral information and spatial structure information of high spatial resolution data (WorldView-2) and medium spatial resolution data (OLI) and (2) evaluate fuzzy classification method for lithological mapping. Firstly

WorldView-2 data and OLI data were structural and spectral combined. Then

texture information and spectral information of the combined data were compressed by PCA

compressed texture layer and compressed spectral layers are selected and stacked. The feature combined data was multi-scale segmented. Finally

the fuzzy logic membership functions of the rock types were built

based on the texture and spectral difference of rock types. And the lithological fuzzy classification of study area was carried out.

Results showed that the proposed method classifies the rock types of study area successfully

and received a high total accuracy of 89.35％.

关键词

Keywords

references

Amer R , Kusky T and Ghulam A . 2010 . Lithological mapping in the central eastern desert of egypt using ASTER data . Journal of African Earth Sciences , 56 ( 2/3 ): 75 - 82 [ DOI: 10.1016/j.jafrearsci.2009.06.004 http://dx.doi.org/10.1016/j.jafrearsci.2009.06.004 ]

Bachri I , Hakdaoui M , Raji M , Teodoro A C and Benbouziane A . 2019 . Machine learning algorithms for automatic lithological mapping using remote sensing data: a case study from souk arbaa sahel, sidi ifni inlier, western anti-atlas, morocco . ISPRS International Journal of Geo-Information , 8 ( 6 ): 248 [ DOI: 10.3390/ijgi8060248 http://dx.doi.org/10.3390/ijgi8060248 ]

Benomar T and Fuling B . 2005 . Improved geological mapping using landsat-5 TM data in Weixi area, Yunnan province China . Geo-spatial Information Science , 8 ( 2 ): 110 - 114 [ DOI: 10.1007/BF02826849 http://dx.doi.org/10.1007/BF02826849 ]

Breiman L . 2001 . Random forests . Machine Learning , 45 ( 1 ): 5 - 32 [ DOI: 10.1023/A:1010933404324 http://dx.doi.org/10.1023/A:1010933404324 ]

Cracknell A P . 1998 . Review article Synergy in remote sensing-what’s in a pixel? International Journal of Remote Sensing , 19 ( 11 ): 2025 - 2047 [ DOI: 10.1080/014311698214848 http://dx.doi.org/10.1080/014311698214848 ]

Congalton R G . 1991 . A review of assessing the accuracy of classifications of remotely sensed data . Remote Sensing of Environment , 37 ( 1 ): 35 - 46 [ DOI: 10.1016/0034-4257(91)90048-B http://dx.doi.org/10.1016/0034-4257(91)90048-B ]

Dorren L K A , Maier B and Seijmonsbergen A C . 2003 . Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification . Forest Ecology and Management , 183 ( 1/3 ): 31 - 46 [ DOI: 10.1016/s0378-1127(03)00113-0 http://dx.doi.org/10.1016/s0378-1127(03)00113-0 ]

Drăguţ L and Blaschke T . 2006 . Automated classification of landform elements using object-based image analysis . Geomorphology , 81 ( 3/4 ): 330 - 344 [ DOI: 10.1016/j.geomorph.2006.04.013 http://dx.doi.org/10.1016/j.geomorph.2006.04.013 ]

Foody G M . 2009 . Sample size determination for image classification accuracy assessment and comparison . International Journal of Remote Sensing , 30 ( 20 ): 5273 - 5291 [ DOI: 10.1080/01431160903130937 http://dx.doi.org/10.1080/01431160903130937 ]

Gad S and Kusky T . 2007 . ASTER spectral ratioing for lithological mapping in the Arabian–Nubian shield, the neoproterozoic Wadi Kid area, Sinai, Egypt . Gondwana Research , 11 ( 3 ): 326 - 335 [ DOI: 10.1016/j.gr.2006.02.010 http://dx.doi.org/10.1016/j.gr.2006.02.010 ]

Gao Y G and Xu H Q . 2015 . Standardization of radiation resolution for fusion of multi-sensor remote sensing images . Journal of Geo-information Science , 17 ( 6 ): 714 - 723

高永刚 , 徐涵秋 . 2015 . 异源遥感影像融合的辐射分辨率规范化方法 . 地球信息科学学报 , 17 ( 6 ): 713 - 723 [ DOI: 10.3724/SP.J.1047.2015.00713 http://dx.doi.org/10.3724/SP.J.1047.2015.00713 ]

Grebby S , Cunningham D , Naden J and Tansey K . 2012 . Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus . International Journal of Earth Sciences , 101 ( 6 ): 1645 - 1660 [ DOI: 10.1007/s00531-011-0742-3 http://dx.doi.org/10.1007/s00531-011-0742-3 ]

Grebby S , Field E and Tansey K . 2016 . Evaluating the use of an object-based approach to lithological mapping in vegetated terrain . Remote Sensing , 8 ( 10 ): 843 [ DOI: 10.3390/rs8100843 http://dx.doi.org/10.3390/rs8100843 ]

Grebby S , Naden J , Cunningham D and Tansey K . 2011 . Integrating airborne multispectral imagery and airborne LiDAR data for enhanced lithological mapping in vegetated terrain . Remote Sensing of Environment , 115 ( 1 ): 214 - 226 [ DOI: 10.1016/j.rse.2010.08.019 http://dx.doi.org/10.1016/j.rse.2010.08.019 ]

Haralick R M , Shanmugam K and Dinstein I H . 1973 . Textural features for image classification . IEEE Transactions on Systems, Man, and Cybernetics, SMC- 3 ( 6 ): 610 - 621 [ DOI: 10.1109/TSMC.1973.4309314 http://dx.doi.org/10.1109/TSMC.1973.4309314 ]

Im J , Jensen J R and Tullis J A . 2008 . Object-based change detection using correlation image analysis and image segmentation . International Journal of Remote Sensing , 29 ( 2 ): 399 - 423 [ DOI: 10.1080/01431160601075582 http://dx.doi.org/10.1080/01431160601075582 ]

Jakob S , Bühler B , Gloaguen R , Breitkreuz C , Eliwa H A and El Gameel K . 2015 . Remote sensing based improvement of the geological map of the Neoproterozoic Ras Gharib segment in the Eastern Desert (NE–Egypt) using texture features . Journal of African Earth Sciences , 111 : 138 - 147 [ DOI: 10.1016/j.jafrearsci.2015.07.024 http://dx.doi.org/10.1016/j.jafrearsci.2015.07.024 ]

Jin X H , He J S , Wang K , Sun Y F and Yang T P . 2016 . Fuzzy classification of land use based on texture and spectrum . Science of Surveying and Mapping , 41 ( 2 ): 49 - 52 , 19

靳晓辉 , 何俊仕 , 王康 , 孙亚飞 , 杨添平 . 2016 . 综合纹理与光谱的土地利用模糊分类 . 测绘科学 , 41 ( 2 ): 49- 52 , 19 [ DOI: 10.16251/j.cnki.1009-2307.2016.02.010 http://dx.doi.org/10.16251/j.cnki.1009-2307.2016.02.010 ]

Kavak K S . 2005 . Recognition of gypsum geohorizons in the Sivas Basin (Turkey) using ASTER and Landsat ETM+ images . International Journal of Remote Sensing , 26 ( 20 ): 4583 - 4596 [ DOI: 10.1080/01431160500185607 http://dx.doi.org/10.1080/01431160500185607 ]

Li N , Frei M and Altermann W . 2011 . Textural and knowledge-based lithological classification of remote sensing data in Southwestern Prieska sub-basin, Transvaal Supergroup, South Africa . Journal of African Earth Sciences , 60 ( 4 ): 237 - 246 [ DOI: 10.1016/j.jafrearsci.2011.03.002 http://dx.doi.org/10.1016/j.jafrearsci.2011.03.002 ]

Li P J , Yu H K and Cheng T . 2009 . Lithologic mapping using ASTER imagery and multivariate texture . Canadian Journal of Remote Sensing , 35 ( S1 ): S117 - S125 [ DOI: 10.5589/m09-019 http://dx.doi.org/10.5589/m09-019 ]

Liu G X , Buckley S M , Ding X L , Chen Q and Luo X J . 2009 . Estimating spatiotemporal ground deformation with improved persistent-scatterer radar interferometry . IEEE Transactions on Geoscience and Remote Sensing , 47 ( 9 ): 3209 - 3219 [ DOI: 10.1109/tgrs.2009.2028797 http://dx.doi.org/10.1109/tgrs.2009.2028797 ]

Lucieer A and Stein A . 2005 . Texture-based landform segmentation of LiDAR imagery . International Journal of Applied Earth Observation and Geoinformation , 6 ( 3/4 ): 261 - 270 [ DOI: 10.1016/j.jag.2004.10.008 http://dx.doi.org/10.1016/j.jag.2004.10.008 ]

Masoumi F , Eslamkish T , Abkar A A , Honarmand M and Harris J R . 2017 . Integration of spectral, thermal, and textural features of ASTER data using Random Forests classification for lithological mapping . Journal of African Earth Sciences , 129 : 445 - 457 [ DOI: 10.1016/j.jafrearsci.2017.01.028 http://dx.doi.org/10.1016/j.jafrearsci.2017.01.028 ]

Myint S W , Gober P , Brazel A , Grossman-Clarke S and Weng Q H . 2011 . Per-pixel vs . object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment , 115 ( 5 ): 1145 - 1161 [ DOI: 10.1016/j.rse.2010.12.017 http://dx.doi.org/10.1016/j.rse.2010.12.017 ]

Othman A A and Gloaguen R . 2014 . Improving lithological mapping by SVM classification of spectral and morphological features: the discovery of a new chromite body in the Mawat ophiolite complex (Kurdistan, NE Iraq) . Remote Sensing , 6 ( 8 ): 6867 - 6896 [ DOI: 10.3390/rs6086867 http://dx.doi.org/10.3390/rs6086867 ]

Othman A A and Gloaguen R . 2017 . Integration of spectral, spatial and morphometric data into lithological mapping: a comparison of different Machine Learning Algorithms in the Kurdistan Region, NE Iraq . Journal of Asian Earth Sciences , 146 : 90 - 102 [ DOI: 10.1016/j.jseaes.2017.05.005 http://dx.doi.org/10.1016/j.jseaes.2017.05.005 ]

Parakh K , Thakur S , Chudasama B , Tirodkar S , Porwal A and Bhattacharya A . 2016 . Machine learning and spectral techniques for lithological classification // Proceedings of the SPIE 9880, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VI . New Delhi : SPIE: 98801 Z [ DOI: 10.1117/12.2223638 http://dx.doi.org/10.1117/12.2223638 ]

Porwal A and González-Álvarez I . 2019 . Introduction to special issue on geologic remote sensing . Ore Geology Reviews , 105 : 216 - 222 [ DOI: 10.1016/j.oregeorev.2018.12.001 http://dx.doi.org/10.1016/j.oregeorev.2018.12.001 ]

Pournamdari M , Hashim M and Pour A B . 2014 . Spectral transformation of ASTER and Landsat TM bands for lithological mapping of Soghan ophiolite complex, south Iran . Advances in Space Research , 54 ( 4 ): 694 - 709 [ DOI: 10.1016/j.asr.2014.04.022 http://dx.doi.org/10.1016/j.asr.2014.04.022 ]

Qiu Y F and Ming D P . 2018 . Lithostratigraphic classification method combining optimal texture window size selection and test sample purification using landsat 8 OLI data . Open Geosciences , 10 ( 1 ): 565 - 581 [ DOI: 10.1515/geo-2018-0045 http://dx.doi.org/10.1515/geo-2018-0045 ]

Sabins F F . 1999 . Remote sensing for mineral exploration . Ore Geology Reviews , 14 ( 3/4 ): 157 - 183 [ DOI: 10.1016/S0169-1368(99)00007-4 http://dx.doi.org/10.1016/S0169-1368(99)00007-4 ]

Salati S , Van Ruitenbeek F J A , Van Der Meer F D , Tangestani M H and Van Der Werff H . 2011 . Lithological mapping and fuzzy set theory: Automated extraction of lithological boundary from ASTER imagery by template matching and spatial accuracy assessment . International Journal of Applied Earth Observation and Geoinformation , 13 ( 5 ): 753 - 765 [ DOI: 10.1016/j.jag.2011.05.004 http://dx.doi.org/10.1016/j.jag.2011.05.004 ]

Salehi T and Tangestani M H . 2018 . Large-scale mapping of iron oxide and hydroxide minerals of Zefreh porphyry copper deposit, using Worldview-3 VNIR data in the Northeastern Isfahan, Iran . International Journal of Applied Earth Observation and Geoinformation , 73 : 156 - 169 [ DOI: 10.1016/j.jag.2018.06.010 http://dx.doi.org/10.1016/j.jag.2018.06.010 ]

Sgavetti M , Pompilio L and Meli S . 2006 . Reflectance spectroscopy (0 . 3 - 2 . 5 µm) at various scales for bulk-rock identification. Geosphere , 2 ( 3 ): 142 - 160 [ DOI: 10.1130/ges00039.1 http://dx.doi.org/10.1130/ges00039.1 ]

Shuai S , Zhang Z , Wang S J and Chen A . 2016 . Lithological mapping by multiple reference spectra based SAM . Journal of Geo-Information Science , 18 ( 1 ): 133 - 140

帅爽 , 张志 , 王少军 , 陈安 . 2016 . 基于多元参照光谱的SAM岩性划分方法 . 地球信息科学学报 , 18 ( 1 ): 133 - 140 [ DOI: 10.3724/SP.J.1047.2016.00133 http://dx.doi.org/10.3724/SP.J.1047.2016.00133 ]

Swanson M D , Kobayashi M and Tewfik A H . 1998 . Multimedia data-embedding and watermarking technologies . Proceedings of the IEEE , 86 ( 6 ): 1064 - 1087 [ DOI: 10.1109/5.687830 http://dx.doi.org/10.1109/5.687830 ]

Tangestani M H , Jaffari L , Vincent R K and Sridhar B B M . 2011 . Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: a case study from Neyriz ophiolite, SW Iran . Remote Sensing of Environment , 115 ( 9 ): 2243 - 2254 [ DOI: 10.1016/j.rse.2011.04.023 http://dx.doi.org/10.1016/j.rse.2011.04.023 ]

Traore M , Wambo J D T , Ndepete C P , Tekin S , Pour A B and Muslim A M . 2020 . Lithological and alteration mineral mapping for alluvial gold exploration in the south east of Birao area, Central African Republic using Landsat-8 Operational Land Imager (OLI) data . Journal of African Earth Sciences , 170 : 103933 [ DOI: 10.1016/j.jafrearsci.2020.103933 http://dx.doi.org/10.1016/j.jafrearsci.2020.103933 ]

Van Der Meer F . 2006 . The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery . International Journal of Applied Earth Observation and Geoinformation , 8 ( 1 ): 3 - 17 [ DOI: 10.1016/j.jag.2005.06.001 http://dx.doi.org/10.1016/j.jag.2005.06.001 ]

Van Der Werff H , Van Ruitenbeek F and Van Der Meer F . 2007 . Geological mapping on Mars by segmentation of hyperspectral OMEGA data // 2007 IEEE International Geoscience and Remote Sensing Symposium . Barcelona : IEEE: 2811 - 2813 [ DOI: 10.1109/IGARSS.2007.4423427 http://dx.doi.org/10.1109/IGARSS.2007.4423427 ]

Wang R S , Xiong S Q , Nie H F , Liang S N , Qi Z R , Yang J Z , Yan B K , Zhao F Y , Fan J H , Tong L Q , Lin J , Gan F P , Chen W , Yang S M , Zhang R J , Ge D Q , Zhang X K , Zhang Z H , Wang P Q , Guo X F and Li L . 2011 . Remote sensing technology and its application in geological exploration . Acta Geologica Sinica , 85 ( 11 ): 1699 - 1743

王润生 , 熊盛青 , 聂洪峰 , 梁树能 , 齐泽荣 , 杨金中 , 闫柏琨 , 赵福岳 , 范景辉 , 童立强 , 林键 , 甘甫平 , 陈微 , 杨苏明 , 张瑞江 , 葛大庆 , 张晓坤 , 张振华 , 王品清 , 郭小方 , 李丽 . 2011 . 遥感地质勘查技术与应用研究 . 地质学报 , 85 ( 11 ): 1699 - 1743 [ DOI: 11-1951/P.20111025.0834.009 http://dx.doi.org/11-1951/P.20111025.0834.009 ]

Whiteside T G , Boggs G S and Maier S W . 2011 . Comparing object-based and pixel-based classifications for mapping savannas . International Journal of Applied Earth Observation and Geoinformation , 13 ( 6 ): 884 - 893 [ DOI: 10.1016/j.jag.2011.06.008 http://dx.doi.org/10.1016/j.jag.2011.06.008 ]

Xu Y J , Meng P Y and Chen J G . 2019 . Study on clues for gold prospecting in the Maizijing-Shulonggou area, Ningxia Hui autonomous region, China, using ALI, ASTER and WorldView-2 imagery . Journal of Visual Communication and Image Representation , 60 : 192 - 205 [ DOI: 10.1016/j.jvcir.2019.02.011 http://dx.doi.org/10.1016/j.jvcir.2019.02.011 ]

Zhang B , Zhang Z , Shuai S and Zhang Y M . 2015 . Lithological mapping by using the synergestic Landsat-8 and WorldView-2 images . Geological Science and Technology Information , 34 ( 3 ): 208 - 213 , 229

张斌 , 张志 , 帅爽 , 张耀明 . 2015 . 利用Landsat-8和WorldView-2数据进行协同岩性分类 . 地质科技情报 , 34 ( 3 ): 208 - 213 , 229

Zhang R S , Zeng M and Chen J P . 2011 . Study on geological structural interpretation based on WorldView-2 remote sensing image and its implementation . Procedia Environmental Sciences , 10 : 653 - 659 [ DOI: 10.1016/j.proenv.2011.09.105 http://dx.doi.org/10.1016/j.proenv.2011.09.105 ]

Zhao B , Wu J J , Yang F , Pilz J and Zhang D H . 2019 . A novel approach for extraction of Gaoshanhe-Group outcrops using Landsat Operational Land Imager (OLI) data in the heavily loess-covered Baoji District, Western China . Ore Geology Reviews , 108 : 88 - 100 [ DOI: 10.1016/j.oregeorev.2018.01.034 http://dx.doi.org/10.1016/j.oregeorev.2018.01.034 ]

Zhou J J , Tian S F , Wang N and Hu X . 2014 . Enhancement and application of WorldView-2 to geological interpretation . Advanced Materials Research , 1010 - 1012 : 1237 - 1242 [ DOI: 10.4028/www.scientific.net/AMR.1010-1012.1237 http://dx.doi.org/10.4028/www.scientific.net/AMR.1010-1012.1237 ]

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