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基于多尺度纹理分类及矿物识别的ASTER地质填图

唐淑兰

唐淑兰. 基于多尺度纹理分类及矿物识别的ASTER地质填图[J]. 地质科技通报, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077
引用本文: 唐淑兰. 基于多尺度纹理分类及矿物识别的ASTER地质填图[J]. 地质科技通报, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077
Tang Shulan. Aster geological mapping based on multi-scale texture classification and mineral recognition[J]. Bulletin of Geological Science and Technology, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077
Citation: Tang Shulan. Aster geological mapping based on multi-scale texture classification and mineral recognition[J]. Bulletin of Geological Science and Technology, 2022, 41(3): 311-320. doi: 10.19509/j.cnki.dzkq.2022.0077

基于多尺度纹理分类及矿物识别的ASTER地质填图

doi: 10.19509/j.cnki.dzkq.2022.0077
基金项目: 

中国地质调查局项目 DD20190364

中国地质调查局项目 202009000000180703

西安财经大学科学研究扶持计划项目 21FCJH008

详细信息
    作者简介:

    唐淑兰(1979—), 女, 讲师, 主要从事遥感影像模糊识别研究。E-mail: mzwsbjh@126.com

  • 中图分类号: P627

Aster geological mapping based on multi-scale texture classification and mineral recognition

  • 摘要:

    遥感技术已经成为基础地质调查必不可少的手段。为提高地质填图效率及精度, 本研究提出了基于ASTER的岩性自动分类加主要和典型造岩矿物识别的填图方法。首先, 对ASTER数据进行主成分变换, 对选取的第一主分量采用Haar小波进行多尺度小波分解, 将小波系数的统计特征作为纹理特征, 构建纹理及光谱多维特征空间; 接着, 运用支持向量机(SVM)进行岩性分类; 同时, 根据光谱特征提取主要造岩矿物; 最后将主要造岩矿物叠加在分类结果上, 结合野外调查背景进行岩性填图。混淆矩阵结果显示光谱+小波纹理分类精度可以达到83.496 2%, 较光谱+灰度共生矩阵纹理分类精度提高了2.675 6%, 较光谱特征分类精度提高了6.318 9%。与最大似然法(MLC)分类相比, SVM分类精度提高了6.623 7%。矿物提取结果表明, 构造的提取指数可有效提取白云母、黑云母、方解石、角闪石等矿物。野外工作证明, 填图结果与野外调查结果的相关系数为0.7, 可见, 基于ASTER数据利用图像处理技术、机器学习算法及波段运算可作为植被覆盖较少地区有效的地质填图手段。

     

  • 图 1  尾亚地区地质图

    Figure 1.  Sketch geological map of Weiya area

    图 2  本研究填图工作流程

    Figure 2.  Flow chart of geological mapping

    图 3  小波纹理特征

    Figure 3.  Wavelet texture features

    图 4  不同移动窗口的小波纹理分类精度

    Figure 4.  Wavelet texture classification accuracy of different moving windows

    图 5  各分解级数的小波纹理分类精度

    Figure 5.  Wavelet texture classification accuracy of each decomposition series

    图 6  不同特征组合分类结果

    Figure 6.  Classification results of different feature combinations

    图 7  方解石提取结果

    Figure 7.  Calcite extraction results

    图 8  矿物提取结果

    Figure 8.  Extraction results for various minerals

    图 9  遥感填图结果

    Q.第四系; C1.下石炭统; Pt2.中元古界; Pt1.下元古界; T2ξ.中三叠石正长岩; T2ηγ.中三叠世二长花岗岩; T2ξο.中三叠世石英正长岩; T2γδ .中三叠世花岗闪长岩; T2υ.中三叠世辉长岩; C2ηγ.晚石炭世二长花岗岩; C2γδ.晚石炭世花岗闪长岩; Pt3gn.新元古代片麻岩

    Figure 9.  Remote sensing mapping results

    表  1  矿物的吸收谱带与ASTER波段的对应关系

    Table  1.   Correspondence relation between absorption bands of minerals and ASTER bands

    矿物 ASTER波段
    1 2 3 4 5 6 7 8 9 10 11 12 13 14
    黑云母 强吸 高反 弱反 高反
    石英 强吸 高反 强吸
    方解石 弱吸 强吸 高反
    正长石 弱反 强吸 高反
    角闪石 弱反 弱吸 强吸 强反
    辉石 高反 强吸
    白云母 高反 强吸 高反
    下载: 导出CSV

    表  2  矿物提取指数

    Table  2.   Extraction index of minerals

    矿物 矿物提取指数
    黑云母 [b12/(b11+b13)]×(b14/b11)
    石英 [b11/(b10+b12)]×(b13/b12)
    方解石 b14/(b13+b12)
    正长石 (b12+b10)/b11
    角闪石 (b6/b8)×(b12/b10)
    辉石 b1/b3
    白云母、绢云母 (b5+b7)/b6
    下载: 导出CSV

    表  3  岩性分类所用样本数

    Table  3.   Sample number for lithological classification

    岩性 Q C1 Pt2 Pt1 T2ξ T2 ηγ T2γδ T2ξo T2υ C2ηγ C2γδ Pt3 gn
    训练样本 1 706 1 737 1 469 951 1 019 1 564 1 469 316 795 1 844 3 639 770
    检验样本 2 460 2 239 2 625 2 248 1 020 1 755 2 191 1 724 439 2 007 1 148 812
    注:岩性同图 1
    下载: 导出CSV

    表  4  不同特征的分类精度

    Table  4.   Classification accuracy of different features

    分类组合 特征维数 总体精度/% Kappa系数
    SF 9 77.177 3 0.747 7
    G-TF 8 61.825 0 0.577 4
    W-TF 8 62.037 9 0.580 1
    SF-G-TF 17 80.820 6 0.787 8
    SF-W-TF 17 83.496 2 0.830 7
    注:SF.光谱;G-TF.GLCM纹理;W-TF.小波纹理;SF-G-TF.光谱+GLCM纹理;SF-W-TF.光谱+小波纹理;下同
    下载: 导出CSV

    表  5  不同分类算法的精度比较

    Table  5.   Accuracy evaluation of different classification algorithms

    特征 SVM法 MLC法
    OA/% Kappa系数 OA/% Kappa系数
    SF 77.177 3 0.747 7 75.248 1 0.720 7
    G-TF 61.825 0 0.577 4 56.023 8 0.516 1
    W-TF 62.037 9 0.580 1 59.067 2 0.547 8
    SF-G-TF 80.820 6 0.787 8 76.035 4 0.735 2
    SF-W-TF 83.496 2 0.830 7 76.872 5 0.744 2
    下载: 导出CSV

    表  6  不同岩性的分类精度

    Table  6.   Classification accuracy of different lithologies

    岩性 SF G-TF W-TF SF-G-TF SF-W-TF
    PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/% PA/% UA/%
    Q 78.01 65.92 81.95 69.04 78.86 73.07 88.46 76.86 88.98 83.84
    C1 97.14 92.63 97.23 80.66 87.05 76.07 97.28 93.20 96.69 97.17
    Pt2 91.73 85.91 75.43 57.42 68.91 72.56 94.10 86.03 94.93 80.88
    Pt1 74.02 85.82 19.17 47.68 58.76 51.70 73.13 84.22 83.67 81.75
    T2ξ 84.02 67.16 92.94 66.39 89.12 62.18 94.41 76.25 93.43 69.97
    T2ηγ 66.04 63.58 62.22 54.22 70.26 50.47 73.39 63.08 73.68 71.20
    T2γδ 95.42 94.81 87.53 96.05 91.71 81.41 95.30 97.10 94.84 92.37
    T2ξo 52.85 76.07 43.96 80.75 54.21 73.68 51.03 73.44 55.13 97.19
    T2υ 68.69 96.23 40.76 74.73 48.74 57.42 73.71 96.02 76.49 92.34
    C2ηγ 58.10 78.78 38.57 37.96 8.32 26.63 60.24 80.49 63.38 92.31
    C2γδ 74.74 46.91 66.55 38.31 47.91 50.60 77.7 59.71 85.89 68.14
    Pt3gn 44.70 55.42 0.12 0.46 7.02 8.65 49.51 57.68 58.25 77.54
    注:岩性代号说明同图 9; PA.生产者精度;UA.用户精度
    下载: 导出CSV
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  • 收稿日期:  2021-12-11

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