唐淑兰, 孟勇, 王国强, 卜涛. 结合多尺度分割和随机森林的变质矿物提取[J]. 工程科学学报, 2022, 44(2): 170-179. DOI: 10.13374/j.issn2095-9389.2020.09.08.004
引用本文: 唐淑兰, 孟勇, 王国强, 卜涛. 结合多尺度分割和随机森林的变质矿物提取[J]. 工程科学学报, 2022, 44(2): 170-179. DOI: 10.13374/j.issn2095-9389.2020.09.08.004
TANG Shu-lan, MENG Yong, WANG Guo-qiang, BU Tao. Extraction of metamorphic minerals by multiscale segmentation combined with random forest[J]. Chinese Journal of Engineering, 2022, 44(2): 170-179. DOI: 10.13374/j.issn2095-9389.2020.09.08.004
Citation: TANG Shu-lan, MENG Yong, WANG Guo-qiang, BU Tao. Extraction of metamorphic minerals by multiscale segmentation combined with random forest[J]. Chinese Journal of Engineering, 2022, 44(2): 170-179. DOI: 10.13374/j.issn2095-9389.2020.09.08.004

结合多尺度分割和随机森林的变质矿物提取

Extraction of metamorphic minerals by multiscale segmentation combined with random forest

  • 摘要: 为提高遥感影像变质矿物提取精度,提升变质带的识别效果,以甘肃北山ASTER影像为研究区,结合了比值运算、多尺度分割、随机森林分类法进行变质矿物提取。首先,通过矿物特征性光谱特征构造比值运算公式、进行影像增强;然后,对增强影像进行基于光谱及变差函数的多尺度分割;接着,采用随机森林法提取目标矿物;最后,通过野外勘查、采样、薄片鉴定进行精度评价。结果表明,黑云母、白云母、角闪石在ASTER影像上具有鉴定性特征,提取精度分别为85.4088%、84.7640%和85.7308%;其他含量较少的变质矿物提取精度可达到60%以上。多尺度分割能充分利用矿物的丛集特征;变差函数纹理能增强形态特征对矿物的区分能力;随机森林分类法对矿物混合引起的噪声不敏感、提取结果稳定。

     

    Abstract: The identification of metamorphic minerals is the basis of metamorphic rock research. Extraction of mineral information by remote sensing technology has been widely used. Digital image processing technology is also effectively applied to remote sensing image processing. Results show that the band ratio of remote sensing images can enhance mineral information, while the variogram function can describe the spatial correlation and variability of image pixels and extract more detailed texture information. The metamorphic minerals are found to present a block or strip distribution. The object-oriented remote sensing image information extraction method can avoid the “salt and pepper phenomenon” based on pixel extraction. Meanwhile, the random forest classification method has a fast calculation speed and high parameter accuracy. It is not sensitive to the noise caused by more lithologic components and its classification effect is found to be stable. To improve the extraction accuracy of metamorphic minerals from remote sensing images and further improve the recognition effect of metamorphic zones, this paper combined the ratio operation, multiscale segmentation, and random forest classification to extract metamorphic mineral information from ASTER images in Beishan area in Gansu Province. Initially, the image was enhanced by the ratio formula of the characteristic spectral structure of the target mineral. Multiscale image segmentation was then performed based on the spectrum and variogram. Finally, the accuracy was evaluated by the thin film identification results of the field exploration samples after the extraction of the target mineral by random forest. Results show that biotite, muscovite, and amphibole have identification characteristics on the ASTER image with an extraction accuracy of 85.4088%, 84.7640%, and 85.7308%, respectively. The extraction accuracy of other metamorphic minerals with less content are found to reach more than 60%. Multiscale segmentation can make full use of the clustering features of minerals and the variogram texture can enhance the ability of morphological features to distinguish the minerals. Random forest is not sensitive to noise and the extraction results are observed to be stable.

     

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