地学前缘 ›› 2009, Vol. 16 ›› Issue (6): 248-256.

• 论文 • 上一篇    下一篇

基于遥感图像地形结构岩性组分分解的岩类多重分形特征研究

潘蔚 倪国强 李瀚波   

  1. 1北京理工大学 光电学院, 北京 100081
    2核工业北京地质研究院 遥感信息与图像分析技术国家重点实验室, 北京 100029
  • 收稿日期:2009-06-10 修回日期:2009-11-15 出版日期:2009-12-16 发布日期:2009-12-10
  • 作者简介:潘蔚(1963—),男,博士研究生,研究员,主要从事铀资源勘查和环境生态监测遥感应用技术研究。Email: panweiprc@yahoo.com.cn
  • 基金资助:

    国防科技遥感重点实验室基金项目(9140C7201010601);国防科工委核能开发专项(YH06112)

A study of RS image landform frame and lithologic component decomposing algorithm and multifractal feature of rock types.

 BO Wei, NI Guo-Jiang, LI Han-Bei   

  1. 1School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China
    2National Key Laboratory of Remote Sensing Information and Image Analysis Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China
  • Received:2009-06-10 Revised:2009-11-15 Online:2009-12-16 Published:2009-12-10

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摘要：

根据光学成像原理和地形结构的分形特征,提出了遥感图像的地形结构岩性组分模型和分离算法,并用于ETM图像分解和岩类αf(α)多重分形特征研究。通过对不同地区二长花岗岩体和沉积变质岩ETM原图像、地形结构子图像和岩性组分子图像的多重分形谱对比分析,发现原始ETM图像的多重分形谱与岩石类型和地形没有明显的对应关系。图像分解后,不同地区的二长花岗岩具有十分相似的岩性组分多重分形谱和不同的地形结构多重分形谱;相反,同一地区的不同类型岩石具有相似的地形结构多重分形谱和不同的岩性组分多重分形谱。因此,利用地形结构岩性组分分类算法,并结合αf(α)多重分形谱分形,可以有效地区分岩石类型。

关键词: 遥感图像;地形结构岩性组分模型;分解算法;岩石类型;αf(α)谱

Abstract:

 Landform framelithologic component decomposing algorithm for remote sensing (RS) image is proposed according to the principle of optical imaging and fractal feature of landform. The algorithm is applied to decomposing the ETM image of rocks for the study of αf(α) multifractal spectrum. The original ETM image, decomposed landform frame subimage and lithologic component subimage are used to calculate the αf(α) multifractal spectra of adamellite and metamorphic sedimentary rocks in different area. With the original ETM image, the αf(α) multifractal spectra do not show any relation to rock types. However, with the decomposed image, the adamellite in different area have the same αf(α) spectra feature of lithologic component subimage and different αf(α) spectra feature of landform frame subimage. On the contrary, the adamellite and metamorphic sedimentary rocks in nearby area have similar αf(α) spectra of landform frame subimage and different αf(α) spectra of lithologic component subimage. This means the landform framelithologic component algorithm and αf(α) multifractal spectrum can provide a new validate method to improve the lithology recognition with RS image texture.

Key words: remote sensing (RS) image; landform framelithologic component model;