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谢涓，闫凯，康志忠，徐箫剑，薛彬，杨建峰，陶金有.2021.“祝融号”火星车多光谱相机岩矿类型识别的地面验证研究.遥感学报，25（7）： 1385-1399
Xie J，Yan K，Kang Z Z，Xu X J，Xue B，Yang J F and Tao J Y. 2021. Verification study for the mineral and rock identification using multispectral camera of the “Zhurong” Mars Rover on the earth. National Remote Sensing Bulletin， 25（7）：1385-1399
中国首个自主火星探测任务“天问一号”探测器于2020-07成功发射，其搭载的中国首台火星车“祝融号”将在着陆火星后开展巡视探测，获取中国火星探测的原始科学数据。识别火星表面岩矿类型对于了解火星的大气变化、环境状态、地质演化以及生物宜居性等具有重要意义。本研究利用“祝融号”火星车搭载的多光谱相机载荷鉴定件（8个波段）在地球环境下拍摄并获取了18种火星表面岩矿的多光谱影像数据。基于该数据所开展的火星岩矿类型的识别研究可为未来基于“祝融号”多光谱相机数据进行火星表面岩矿的原位识别提供指导。本文所采用的基于多光谱影像波段运算和彩色图像HSV颜色特征直方图的岩矿识别方法，可从多光谱特征和颜色特征两个层面来识别不同类型的岩矿。所使用的多光谱影像数据是在太阳高度角约60°、拍摄高度1.8 m、拍摄角度约37°的条件下采集所得。在该实验条件下，两种识别方法共提取出12种岩矿的识别指标。基于多光谱影像波段运算可以区分出在8个特定波段下具有突出光谱特征的岩矿类型，而基于图像HSV颜色特征可区分出颜色特征分布较为集中的岩矿类型。同时，研究发现拍摄角度不同会对岩矿的识别效果产生不可忽略的影响。
China’s first autonomous Mars exploration mission probe “Tianwen-1” was successfully launched in July 2020, and the rover of “Tianwen-1” successfully landed on the pre-selected landing area in the southern Utopia Plain of Mars on May 15. After landing on Mars, China’s first Mars rover “Zhurong” will carry out inspections and bring the raw scientific data for Chinese Mars exploration. The multispectral camera mounted on the “Zhurong” rover can be used to study the types of mineral and rock near the landing area. Identifying the types of mineral and rock on Mars is of great significance for understanding the Martian atmospheric change, environmental conditions, geological evolution, and future livability. The study in this article selected 18 kinds of common mineral and rock on the surface of Mars, and shot these mineral and rock with the same multispectral camera as on “Zhurong” rover in the earth environment. Then, this study uses the obtained multispectral image carry out the research of mineral and rock identification, hoping to provide guidance for future in-situ identification of mineral and rock on the surface of Mars based on the “Zhurong” multispectral camera data. Considering that 8-band spectral data is not enough to capture the spectral characteristics of all mineral and rock, it will result in fewer mineral and rock identified, this paper uses color features to assist in the identification of mineral and rock. This paper uses band operation based on multispectral image and HSV color feature histogram extraction based on color image to identify different types of mineral and rock. The above method can identify different types of mineral and rock from the perspective of multispectral features and color features. This study collected multispectral images of mineral and rock under three different shooting conditions, and found that the spectral characteristics of mineral and rock may change under different shooting conditions, which will have an impact on mineral and rock identification. The multispectral image data used in this paper was collected under the conditions of the solar elevation angle at about 60 °, the shooting height at 1.8 meters and the shooting angle at about 37°. Under this condition, the research method in this paper has extracted identification indexes of 12 kinds of mineral and rock. The identification indexes corresponding to specific mineral and rock are listed in the article, all results are based on the above shooting condition. The study shows that: using the band operation based on multispectral image, mineral and rock with prominent spectral characteristics under 8 specific bands can be identified; and using color feature histogram extracted from HSV color image, mineral and rock with a relatively concentrated color feature can be identified. In addition, since different shooting conditions will have an impact on the identification of mineral and rock, it is necessary to do the identification research under different conditions in the future.
天问一号祝融号多光谱影像波段运算颜色特征岩矿识别
Tianwen-1Zhurongmultispectral imageband operationcolor featuremineral and rock identification
Carter J, Poulet F, Bibring J P, Mangold N andMurchie S. 2013. Hydrous minerals on Mars as seen by the CRISM and OMEGA imaging spectrometers: Updated global view. Journal of Geophysical Research: Planets, 118(4) [DOI: B10.1029/2012JE004145]
Deng J Y and Chen J P. 2018. Quantitative analysis of multi spectral image band ratio method to eliminate terrain shadow. Journal of Geology, 042(003):501-506
邓佳音, 陈建平. 2018.多光谱影像波段比值法消除地形阴影的定量分. 地质学刊，042(003):501-506 [DOI: CNKI:SUN:JSDZ.0.2018-03-023http://dx.doi.org/CNKI:SUN:JSDZ.0.2018-03-023]
Gou S, Yue Z Y, Di K C and Zhang X. 2017. Advances in aqueous minerals detection on Martian surface. Journal of Remote Sensing, 021(004):531-548
芶盛, 岳宗玉, 邸凯昌, 张霞. 2017. 火星表面含水矿物探测进展. 遥感学报, 021(004):531-548 [DOI: 10.11834/jrs.20176335http://dx.doi.org/10.11834/jrs.20176335]
Han D, Wu P, Zhang Q, Han G D and Tong F. 2016.Feature extraction and image recognition of typical grassland forage based on color moment.Transactions of the Chinese Society of Agricultural Engineering. 032(023):168-175
韩丁, 武佩, 张强, 韩国栋，通霏. 2016. 基于颜色矩的典型草原牧草特征提取与图像识别. 农业工程学，032(023):168-175 [DOI: 10.11975/j.issn.1002-6819.2016.23.023http://dx.doi.org/10.11975/j.issn.1002-6819.2016.23.023]
He Y T and Ke C Q. 2008. Application of semi variance function texture extraction in remote sensing image classification. Remote Sensing Technology and Application,023(005):571-575
何宇婷, 柯长青. 2008. 半方差函数纹理提取在遥感图像分类中的应用. 遥感技术与应用, 023(005):571-575 [DOI: 10.11873/j.issn.1004-0323.2008.5.571http://dx.doi.org/10.11873/j.issn.1004-0323.2008.5.571]
Huang R and Hu M. 2014. Content-based Image Retrieval Using Color Position and Texture Fused Features. Computer Science, 118-121
黄仁，胡敏. 2014. 综合颜色空间特征和纹理特征的图像检索. 计算机科学, 118-121 [DOI: CNKI:SUN:JSJA.0.2014-S1-026]
Li C L, Liu J J, Geng Y, Cao J B, Zhang T L, Fang G Y, Yang J F, Shu R, Zou Y L, Lin Y T and Ouyang Z Y. 2018. Scientific objectives and payload configuration of China’s first Mars exploration mission. Journal of Deep Space Exploration, volume 5(5): 406-413
李春来, 刘建军, 耿言, 曹晋滨, 张铁龙, 方广有, 杨建峰, 舒嵘, 邹永廖, 林杨挺, 欧阳自远. 2018. 中国首次火星探测任务科学目标与有效载荷配置. 深空探测学报, 5(5):406-413 [DOI:10.15982/j.issn.2095-7777.2018.05.002http://dx.doi.org/10.15982/j.issn.2095-7777.2018.05.002]
Lin H L. 2018. Hyperspectral remote sensing method for fine classification of Hydrous Minerals in Mars.Beijing:Chinese Academy of Sciences
林红磊. 2018.火星含水矿物精细类别的高光谱遥感探测方法研究
Ody A, Poulet F, Langevin Y, Bibring J P, Bellucci G, Altieri F, Gondet B, Vincendon M, Carter J and Manaud N. Global maps of anhydrous minerals at the surface of Mars from OMEGA/MEX. 2012. Journal of Geophysical Research, 117: E00J14 [DOI: 10.1029/2012JE004117http://dx.doi.org/10.1029/2012JE004117]
Pelkey S M, Mustard J F, Murchie S, Clancy R, Wolff M, Smith M, Milliken R, Bibring J P, Gendrin A and Poulet F. 2007, CRISM multispectral summary products: Parameterizing mineral diversity on Mars from reflectance. Journal of Geophysical Research Planets, 112(E8):171-178 [DOI: 10.1029/2006JE002831http://dx.doi.org/10.1029/2006JE002831]
Poulet F, Gomez C, Bibring JP, Langevin Y, Gondet B, Pinet P, Belluci G and Mustard J. 2007. Martian surface mineralogy from observatoire pour la minéralogie, l'eau, les glaces et l'activitéon board the mars express spacecraft (omega/mex): global mineral maps. Journal of Geophysical Research Planets, 112(E8) [DOI: 10.1029/2006JE002840]
Salvatore M R, Goudge T A, Bramble M S, Edward C S, Bandfield J L, Amador E S, Mustard J F and Christensen P R. Bulk mineralogy of the NE Syrtis and Jezero crater regions of Mars derived through thermal infrared spectral analyses[J].Icarus, 2017: S0019103517302944 [DOI: 10.1016/j.icarus.2017.09.019http://dx.doi.org/10.1016/j.icarus.2017.09.019]
Song B J. 2019. Application of unsupervised clustering algorithm in rock image analysis.Xi’an: Xi`an Shiyou University
宋博敬. 2019.非监督聚类算法在岩石图像分析中的应用研究.西安：西安石油大学
Tang S L, Cao J N and Wang K. 2021. Remote sensing mineralization alteration information extraction based on PCA, Multilevel Segment Method, and SVM. Journal of Remote Sensing, 25(2):653-664
唐淑兰, 曹建农, 王凯. 2021. 结合PCA、多尺度分割及SVM的ASTER遥感蚀变信息提取. 遥感学报,25(2):653-664 [DOI: 10.11834/jrs.20219091http://dx.doi.org/10.11834/jrs.20219091]
Victoria E,Hamilton, Philip R and Christensen. 2005. Evidence for extensive, olivine-rich bedrock on Mars. Geology; 33(6): 433-436 [DOI: https://doi.org/10.1130/G21258.1https://doi.org/10.1130/G21258.1]
Viviano-Beck, C E, Seelos, F P, Murchie, S L,Kahn E G, Seelos K D, Taylor, H W, Taylor Kelly, Ehlmann, B L, Wiseman S M, Mustard J F, and Morgan M F. 2014. Revised CRISM spectral parameters and summary products based on the currently detected mineral diversity on Mars, J. Geophys. Res. Planets,119,1403-1431 [DOI: 10.1002/2014je004627http://dx.doi.org/10.1002/2014je004627]
Wei J L. 2017. Silicate rock remote sensing recognition based on thermal radiation and texture features.Beijing:China University of Geoscience(beijing) (韦佳黎. 2017. 基于热辐射与纹理特征的硅酸盐岩石遥感识别.北京：中国地质大学(北京)) [DOI: CNKI:CDMD:2.1017.136029]
Wei J, Ming Y F, Han L S, Ren Z L and Guo Y M. 2015. Mineral information remote sensing recognition method based on multi type spectral feature parameter matching. Spectroscopy and Spectral Analysis, 190-194
韦晶, 明艳芳, 韩留生, 任仲亮, 郭亚敏. 2015.基于多类型光谱特征参数匹配的矿物信息遥感识别方法. 光谱学与光谱分析,190-194) [DOI: 10.3964/j.issn.1000-0593(201510-2862-05]
Xu Q Land Yi J H. 2014. A calculation method of optimal band combination of TM remote sensing image based on optimal exponential model. Geomatics & Spatial Information Technology, 000(004):32-35
许泉立, 易俊华. 2014. 一种基于最佳指数模型的TM遥感影像最佳波段组合计算方法.测绘与空间地理信息, 000(004):32-35 [DOI: 10.3969/j.issn.1672-5867.2014.04.008http://dx.doi.org/10.3969/j.issn.1672-5867.2014.04.008]
Xu W H. 2010. Texture analysis and classification of remote sensing image based on Fractal Theory.Changsha:Central South University
徐文海. 2010. 基于分形理论的遥感影像纹理分析与分类研究. 长沙： 中南大学) [DOI: 10.7666/d.y1718850http://dx.doi.org/10.7666/d.y1718850]
Yang Y, Jin S G and Xue Y S. 2016. Exploration and evolution of water bearing minerals on the surface of Mars using CRISM data. Journal Of Deep Space Exploration, 3(2): 187-194
杨懿, 金双根, 薛岩松. 2016. 利用CRISM数据探测火星表面含水矿物及其演化. 深空探测学报, 3(2): 187-194 [DOI: 10.15982/j.issn.2095-7777.2016.02.015http://dx.doi.org/10.15982/j.issn.2095-7777.2016.02.015]
Zhan W F, Chen Y H, Ma W, Zhou J and Li J. 2010. Analysis of Field of View Effect of Bright Temperature Observation in City Target Direction. Journal of Remote Sensing, (02):372-395
占文凤, 陈云浩, 马伟, 周纪, 李京. 2010. 城市目标方向亮温观测的视场效应分析. 遥感学报, (02):372-395 [DOI: 10.11834/jrs.20100213]
Zhang J, Sokhansanj S, Wu S, Fang R, Yang W and Winter P. 1998. A transformation technique from RGB signals to the Munsell system for color analysis of tobacco leaves. Computers and Electronics in Agriculture, 19(2):155-166 [DOI: 10.1016/S0168-1699(97)00041-0http://dx.doi.org/10.1016/S0168-1699(97)00041-0]
Zhang X, Wu X, Lin H L and Wang N. 2018. Retrieval of mineral abundances of delta region in Eberswalde, Mars. Journal of Remote Sensing, 22(2):304-312
张霞, 吴兴, 林红磊, 王楠. 2018. 火星Eberswalde撞击坑三角洲矿物丰度反演. 遥感学报, 22(2):304-312 [DOI: 10.11834/jrs.20187046http://dx.doi.org/10.11834/jrs.20187046]
Zhao Y, Xie D H, Deng L, Yan Y N and Li B X. 2018. Method and system realization for calculating BRDF using multi-angle images. Remote Sensing Technology and Application, 033(004):741-749
赵云, 谢东海, 邓磊, 闫亚男, 李博旭. 2018. 利用多角度影像计算BRDF的方法与系统实现. 遥感技术与应用, 033(004):741-749 [DOI: 10.11873/j.issn.1004-0323.2018.4.0741http://dx.doi.org/10.11873/j.issn.1004-0323.2018.4.0741]
Zhu M Q, Zhou W P and Hu Q Y. 2010. New progress in hyperspectral exploration of mineral composition by Mars Express Omega. Advances in Earth Science. 25(7):691-697
祝民强, 周万蓬, 胡全一. 2010. 火星快车OMEGA高光谱探测矿物组成的新进展. 地球科学进展, 25(7):691-697 [DOI: 10.11867/j.issn.1001-8166.2010.07.0691http://dx.doi.org/10.11867/j.issn.1001-8166.2010.07.0691]
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