Please wait a minute...
 
国土资源遥感  2014, Vol. 26 Issue (2): 60-68    DOI: 10.6046/gtzyyg.2014.02.11
  技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于二次散射非线性混合模型的矿物填图方法
余先川1, 熊利平1, 徐金东1, 胡丹1, 张立保1, 李建广2
1. 北京师范大学信息科学与技术学院, 北京 100875;
2. 中国传媒大学信息工程学院, 北京 100024
Mineral mapping based on secondary scattering mixture model
YU Xianchuan1, Xiong Liping1, XU Jindong1, Hu Dan1, ZHANG Libao1, LI Jianguang2
1. Beijing Normal University, College of Information Science and Technology, Beijing 100875, China;
2. Communication University of China, Information Enginering School, Beijing 100024, China
全文: PDF(9043 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

传统的遥感地质填图方法较少考虑到一个像元中多种地物共生存在的情况,因此所填图件难以反映矿物的分布特征。针对线性混合模型解混精度不高的问题,使用二次散射非线性混合模型对高光谱数据进行光谱解混,并在此基础上,提出了kk≥2)类地物的填图规则。采用美国内华达州Cuprite地区AVIRIS数据进行填图实验,将其结果与Clark等的填图结果进行对比。实验结果表明:与线性模型的矿物填图相比,基于二次散射非线性混合模型所填图件更加接近矿物的真实分布;使用kk≥2)类矿物填图规则的填图结果细节丰富,与Clark等人的填图结果吻合度高。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
白淑英
史建桥
沈渭寿
高吉喜
王冠军
关键词 西藏雪深时空变化气候响应    
Abstract

Traditional geological mapping methods usually cannot conduct mapping for the whole study area and takes little account to the situation that a variety of features has symbiotic combination in one pixel, which makes it difficult to reflect the complex geological distribution characteristics. Since the unmixing accuracy of the linear model cannot meet actual application need, the secondary scattering model was used to the unmixing of hyperspectral data. On such a basis, this paper proposed k (k ≥ 2) class mapping rules based on the unmixing result. The Nevada Cuprite AVIRIS data were used in the experiment, and actual mapping results obtained by Clark et al. were taken as the reference. The comparison results have shown that mapping results based on the secondary scattering mixture model are closer to actual ground feature distribution than those based on the linear model and, in comparison with the results from one class mapping rule, the results using k (k ≥ 2) class mapping rules have richer details and are closer to the results obtained by Clark et al.

Key wordsTibet    snow depth    spatial-temporal variations    climate response
收稿日期: 2013-04-15      出版日期: 2014-03-28
:  TP79  
基金资助:

国家自然科学基金项目(编号:41272359,41072245)和教育部博士点基金(编号:20120003110032)共同资助。

作者简介: 余先川(1967- ),男,教授,博士生导师。主要从事影像处理、数学地质及空间信息处理等研究。Email:yuxianchuan@163.com。
引用本文:   
余先川, 熊利平, 徐金东, 胡丹, 张立保, 李建广. 基于二次散射非线性混合模型的矿物填图方法[J]. 国土资源遥感, 2014, 26(2): 60-68.
YU Xianchuan, Xiong Liping, XU Jindong, Hu Dan, ZHANG Libao, LI Jianguang. Mineral mapping based on secondary scattering mixture model. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 60-68.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gtzyyg.2014.02.11      或      https://www.gtzyyg.com/CN/Y2014/V26/I2/60

[1] 张良培,张立福.高光谱遥感[M].北京:测绘出版社,2011:1-4. Zhang L P,Zhang L F.Hyperspectral remote sensing[M].Beijing:Surveying and Mapping Press,2011:1-4.

[2] 王润生,甘甫平,闫柏琨,等.高光谱矿物填图技术与应用研究[J].国土资源遥感,2010,22(1):1-13. Wang R S,Gan F P,Yan B K,et al.Hyperspectral mineral mapping and its application[J].Remote Sensing for Land and Resources,2010,22(1):1-13.

[3] Keshava N,Mustard J F.Spectral unmixing[J].Signal Processing Magazine,IEEE,2002,19(1):44-57.

[4] 余先川,李建广,徐金东,等.基于二次散射的高光谱遥感图像光谱非线性混合模型[J].国土资源遥感,2013,25(1):18-25. Yu X C,Li J G,Xu J D,et al.A nonlinear spectral mixture model for hyperspectral imagery based on secondary scattering[J].Remote Sendsing for Land and Resources,2013,25(1):18-25.

[5] Altmann Y,Halimi A,Dobigeon N,et al.A post nonlinear mixing model for hyperspectral images unmixing[C]//Procleding of the IEEE in ternationd Geoscience and Remote Sensing Symposium(IGARSS).Vancouver,BC:IEEE,2011:1882-1885.

[6] Vaughan R G,Hook S J,Calvin W M,et al.Surface mineral mapping at Steamboat Springs,Nevada,USA,with multi-wavelength thermal infrared images[J].Remote Sensing of Environment,2005,99(1):140-158.

[7] Kruse F A,Boardman J W,Huntington J F.Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(6):1388-1400.

[8] Hunt G R,Salisbury J W,Lenhoff C J.Visible and near infrared spectra of minerals and rocks VI Additional silicates[J].Modern Geology,1973(4):85-106.

[9] 甘甫平,王润生,马蔼乃,等.遥感地质信息提取集成与矿物遥感地质分析模型[J].遥感学报,2003,7(3):207-213. Gan F P,Wang R S,Ma A N,et al.Integration for extracting and mineral analysis models for geological application using remote sensing data[J].Journal of Remote Sensing,2003,7(3):207-213.

[10] Kruse F A,Lefkoff A B,Boardman J W,et al.The spectral image processing system(SIPS)—Interactive visualization and analysis of imaging spectrometer data[J].Remote Sensing of Environment,1993,44(2/3):145-163.

[11] Boardman J W,Kruse F A,Green R O.Mapping target signatures via partial unmixing of AVIRIS data[J].Proceedings of the Fifth JPL Airborne Earth Science Workshop,1995,95(1):23-26.

[12] Nascimento J M P,Bioucas Dias J M B.Vertex component analysis:A fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(4):898-910.

[13] Nascimento J M P,Bioucas-Dias J M.Nonlinear mixture model for hyperspectral unmixing[C]//SPIE Europe Remote Sensing.International Society for Optics and Photonics,2009:74770I-74770I.

[14] Ju J C,Kolaczyk E D,Gopal S.Gaussian mixture discriminant anlaysis and sub-pixel land cover characterization in remote sensing[J].Remote Sensing of Environment,2003,84(4):550-560.

[15] Iordache M D,Bioucas-Dias J M,Plaza A.Sparse unmixing of hyperspectral data[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(6):2014-2039.

[16] Heinz D C.Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):529-545.

[17] RSI(Research Systems Inc.).ENVI User's guide Version 4.0[Z].Boulder,CO 80301 USA,2003.

[18] AVIRIS data of Cuprite district,America.http://aviris.jpl.nasa.gov/html/aviris.freedata.html.

[19] Vane G,Green R O,Chrien T G,et al.The airborne visible/infrared imaging spectrometer(AVIRIS)[J].Remote Sensing of Environment,1993,44(2):127-143.

[20] Clark,Swayze.Summaries of the 6th annual JPL airborne earth science workshop march 4-8,1996[Z].http://speclab.cr.usgs.gov/map.intro.html.

[1] 胡盈盈, 戴声佩, 罗红霞, 李海亮, 李茂芬, 郑倩, 禹萱, 李宁. 2001—2015年海南岛橡胶林物候时空变化特征分析[J]. 自然资源遥感, 2022, 34(1): 210-217.
[2] 范田亿, 张翔, 黄兵, 钱湛, 姜恒. 湘江流域TRMM卫星降水产品降尺度研究与应用[J]. 自然资源遥感, 2021, 33(4): 209-218.
[3] 晋成名, 杨兴旺, 景海涛. 基于RS的陕北地区植被覆盖度变化及驱动力研究[J]. 自然资源遥感, 2021, 33(4): 258-264.
[4] 杨蕴雪, 张艳芳. 基于空间距离指数的延河流域生态敏感性时空演变特征[J]. 自然资源遥感, 2021, 33(3): 229-237.
[5] 叶婉桐, 陈一鸿, 陆胤昊, 吴鹏海. 基于GEE的2000—2019年间升金湖湿地不同季节地表温度时空变化及地表类型响应[J]. 国土资源遥感, 2021, 33(2): 228-236.
[6] 陈虹, 郭兆成, 贺鹏. 1988—2018年间洱海流域植被覆盖度时空变换特征探究[J]. 国土资源遥感, 2021, 33(2): 116-123.
[7] 潘梦, 曹云刚. 高亚洲地区冰湖遥感研究进展与展望[J]. 国土资源遥感, 2021, 33(1): 1-8.
[8] 王平, 毛克彪, 孟飞, 袁紫晋. 中国东海海表温度时空演化分析[J]. 国土资源遥感, 2020, 32(4): 227-235.
[9] 赵冰, 毛克彪, 蔡玉林, 孟祥金. 中国地表温度时空演变规律研究[J]. 国土资源遥感, 2020, 32(2): 233-240.
[10] 王海庆, 郝建亭, 李丽, 安娜, 许文佳, 殷亚秋. 西藏各行政区矿产开采强度遥感分析[J]. 国土资源遥感, 2020, 32(1): 115-119.
[11] 王伟, 阿里木·赛买提, 吉力力·阿不都外力. 基于地理探测器模型的中亚NDVI时空变化特征及其驱动因子分析[J]. 国土资源遥感, 2019, 31(4): 32-40.
[12] 王海庆, 李丽, 陈玲, 许文佳, 杨金中, 刘琼. 基于尾矿库调查的西藏自治区金属矿开采强度分析[J]. 国土资源遥感, 2019, 31(2): 218-223.
[13] 韩红珠, 白建军, 张波, 马高. 基于MODIS时序的陕西省植被物候时空变化特征分析[J]. 国土资源遥感, 2018, 30(4): 125-131.
[14] 杨敏, 杨贵军, 王艳杰, 张勇峰, 张智宏, 孙晨红. 北京城市热岛效应时空变化遥感分析[J]. 国土资源遥感, 2018, 30(3): 213-223.
[15] 王阳明, 张景发, 刘智荣, 申旭辉. 基于多源遥感数据西藏山南地区活动断层解译[J]. 国土资源遥感, 2018, 30(3): 230-237.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发