Geological Units Classification with Texture-Spectral Synergy of Multi-Sourced Remote Sensing Images
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摘要: 岩石单元的结构、构造、差异风化和出露状况在遥感图像上综合表现为图形纹理特征即“图”标志，其矿物成分和组合则表现为光谱特征即“谱”标志.传统遥感岩石单元分类以利用其光谱特征为主，图形纹理特征为辅，因此分类精度有限.以新疆维吾尔自治区与甘肃省交界的北山西段为研究区，开展岩石单元图形指数和光谱指数协同分类方法研究.基于Worldview-2全色图像构建的图形指数，能够量化岩石单元的层理、构造、展布形态和微地貌等特征，包括0°和45°定向滤波图像及灰度共生矩阵计算出的同质性和异质性特征图像、熵特征图像；光谱指数基于Worldview-2多光谱图像和ASTER（Advanced Spaceborne Thermal Emission and Reflection Radiometer）短波红外波段图像利用比值、和-差方法构建.多源遥感图像构建的光谱指数其光谱波段涵盖可见光-近红外及短波红外，包括RI（Ratio index）ASTER、SI（Spectral index）ASTER、SIWorldview-2.采用面向对象方法对建立的图谱指数进行多尺度分割，依据不同岩石单元出露规模建立适宜的分割尺度，利用光谱指数自动提取相应岩石信息，实现岩石单元自动分类.结果表明，实验区基于图谱协同方法共划分出17类岩石单元，总体精度达到83.62%，而单独利用Worldview-2和ASTER图像，仅划分出13类和14类岩石单元.提出的图谱协同岩石分类方法可为我国西部高海拔深切割无人区地质调查及找矿工作提供新思路和遥感技术支撑.Abstract: The structural pattern, differential weathering, and outcrop situation of different geological units can be described by the texture information of remote sensing imagery as graphical features. The geological units compositions of differential minerals are shown as spectral cues.With regard to geological units classification, the classification accuracy of most studies is limited since they have mainly utilized the spectral cues from multis-pectral or hyperspectral images to characterize their spectral features, and high resolution imagery to depict the texture information as a supplement. In this study, the texture-spectral indices are established with the multi-sourced remote sensing images of Worldview-2 and ASTER(Advanced Spaceborne Thermal Emission and Reflection Radiometer)and tested for geological units classification in the western section of Beishan Mountain, which are located at the junction of Gansu Province and Xinjiang Uygur Autonomous Region. Based on the panchromatic band of Worldview-2 imagery, the following graphical features that quantify the bedding structure, distribution morphology and micro-topography features of different rock units were extracted: 0° and 45°directional filtering, the textures of homogeneity, dissimilarity and entropy generated from gray level co-occurrence matrix. Based on multi-spectral bands of Worldview-2 imagery and the short-wave infrared bands of ASTER imagery, the spectral indices were established by the band-ratio and addition-difference methods, including RI (Ratio index)ASTER, SI(Spectral index) ASTER, and SIWorldview-2. First, based on the object-oriented approach and the texture-spectral indices, texture-spectral features were used to conduct multi-resolution segmentation to produce numerous geological units with different scales. Second, the geological units were classified at different scales using spectral indices. The results show that: (1) 17 types of geological units were classified with an overall accuracy of 83.62%, based on the proposed method; (2) and only 13 or 14 types of geological units were classified, by using the Worldview-2 or ASTER images, respectively. The outlined approach could provide the theoretical and remote sensing technical support for geological survey and prospecting work in the high altitude with deep-cut unpopulated areas of western China.
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图 2 研究区图形特征
图a中RGB分别为Worldview-2全色波段、全色波段45°定向滤波、熵特征；图b，c，d为图a红色框内同质性和异质性特征图像、熵特征图像；图e，f为图a绿色框内全色波段0°、45°定向滤波图像
Fig. 2. Texture features of the study area
图 3 主要岩石单元光谱曲线图及去包络线光谱图
a. ASTER光谱; b. Worldview⁃2光谱; c. ASTER光谱去包络线; d. Worldview⁃2光谱去包络线. 1.花岗岩; 2.闪长玢岩; 3.二长花岗岩; 4.正长花岗岩; 5.花岗闪长岩; 6.绿泥片岩阳起片岩; 7.黑云二长花岗岩; 8.安山玄武岩夹玄武岩; 9.凝灰岩夹凝灰质砂岩; 10.玄武质凝灰岩; 11.砂质板岩; 12.玄武岩; 13.玄武岩夹凝灰质砂岩; 14.大理岩; 15.板岩; 16.千枚状板岩; 17.英安岩夹凝灰岩
Fig. 3. Geological units spectra before and after continuum removal
图 4 研究区多尺度分割图
a、b、c、d、e、f分割尺度依次为200、500、800、1 000、3 000、5 000;RGB组合为SIWorldview-2指数特征波段、异质性特征波段及Worldview-2第8波段
Fig. 4. Multi-scale segmentation map of the study area
图 5 研究区基于不同数据源的岩石单元分类
1.玄武岩夹凝灰质砂岩；2.大理岩；3.绿泥片岩阳起片岩；4.闪长玢岩；5.花岗岩；6.正长花岗岩；7.花岗闪长岩；8板岩；9.英安岩夹凝灰岩；10.二长花岗岩；11.凝灰岩夹凝灰质砂岩；12.砂质板岩；13.玄武岩；14.安山玄武岩夹玄武岩；15.黑云二长花岗岩；16.千枚状板岩；17.玄武质凝灰岩
Fig. 5. Geological units classification maps of the study area based on different data
表 1 遥感数据源信息
Table 1. The remote sensing data
数据源 成像时间 空间分辨率(m) 光谱分辨率(nm) 光谱范围(nm) 波段数 ASTER 2002-10-22 可见光-近红外/15，短波/30 可见光-近红外约70，短波约50 556~2 400 9 Worldview-2 2015-03-11 全色/0.5，多光谱/1.8 可见光约80，近红外约100 400~1 040 8 
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表 2 于不同数据源岩石单元分类精度
Table 2. Accuracy of geological units classification based on different data
岩石单元 图谱协同 Worldview-2数据 ASTER数据 生产者精度（%） 用户精度（%） 生产者精度（%） 用户精度（%） 生产者精度（%） 用户精度（%） 花岗岩 77.86 96.46 - - 53.8 64.7 闪长玢岩 74.22 60.70 80.55 52.72 42.47 43.66 二长花岗岩 92.13 93.47 90.29 86.96 96.71 97.55 正常花岗岩 95.38 78.98 85.39 69.34 75.00 60.74 花岗闪长岩 86.72 77.89 33.77 44.95 77.69 82.28 绿泥片岩阳起片岩 85.11 68.03 29.10 44.62 66.28 67.34 黑云二长花岗岩 80.36 95.11 67.67 55.61 78.09 96.03 安山玄武岩夹玄武岩 65.92 97.62 67.96 56.37 - - 凝灰岩夹凝灰质砂岩 97.19 68.40 23.64 36.39 77.08 69.08 玄武质凝灰岩 96.33 73.68 26.04 47.30 77.08 90.63 砂质板岩 96.77 71.01 - - - - 玄武岩 98.70 72.02 - - - - 玄武岩夹凝灰质砂岩 63.60 83.19 - - 72.44 77.93 大理岩 85.62 90.30 83.42 76.42 98.88 97.11 板岩 66.11 80.41 38.42 25.81 51.09 62.75 千枚状板岩 83.64 92.52 20.79 35.16 85.04 45.24 英安岩夹凝灰岩 77.64 85.27 36.66 46.31 61.22 60.24 Kappa系数 0.819 0 0.534 8 0.748 0 总体精度(%) 83.62 56.70 77.09
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