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纸质出版日期： 2023-07-07 ，
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唐淑兰，孟勇.2023.结合小波变换、SVM和投票法的ASTER影像岩性分类——以东天山尾亚地区为例.遥感学报，27（7）： 1702-1712
Tang S L and Meng Y. 2023. Lithological classification from the ASTER data based on wavelet transform， SVM， and voting methods： A case study for the Weiya area in the eastern Tian Shan. National Remote Sensing Bulletin， 27（7）：1702-1712
为了更加准确地利用ASTER影像辅助填图，提出了一种结合小波变换、支持向量机（SVM）和投票法的ASTER影像岩性自动分类方法。首先，采用Haar小波对ASTER影像进行多尺度小波分解，统计小波系数的均值作为纹理特征，同时提取灰度共生矩阵（GLCM）方差、同质性、均值纹理特征；然后，利用小波纹理、GLCM纹理及光谱特征构造SVM分类的特征向量，并进行10次重复分类；最后利用投票法确定岩性单元。对结果进行统计评估，结合多种纹理，并利用投票法得到的岩性分类精度为92.1934%，Kappa系数为0.9202，比仅用光谱分类精度提高了13.3369%。小波纹理能提取更细节的岩性信息；投票法可以避免岩性因样本的空间变异性产生的动态变化，优化分类结果；SVM较最大似然法（MLC）更适合于训练数据集高维且非正态分布的岩性分类；采用人工蜂群算法搜索SVM的最优参数，可避免参数局部最优。
In ASTER images
different lithologic units show obvious multiscale texture features
and wavelet transform has the advantage of extracting multiscale features. Support Vector Machine (SVM) is suitable for solving the classification problem of little training data and nonnormal data distribution. SVM is used to complete lithology classification. The classification results have high classification accuracy and low uncertainty. Using the voting method in selecting lithologic classification results can avoid the uncertainty of lithologic classification results caused by the extraction method of lithologic samples
thereby making the classification results statistically significant. An automatic classification method for ASTER image lithology integrating the wavelet texture
SVM
and voting method is proposed to improve the accuracy of ASTER imagery exploited for mapping assistance. First
the Haar wavelet is utilized for decomposing the ASTER image involving a multiscale wavelet
with the mean value of wavelet coefficients considered texture features. Moreover
the variance
homogeneity
and mean values of the gray-level co-occurrence matrix (GLCM) are extracted concurrently. Then
the feature vectors of the SVM classification are constructed with multiscale texture
GLCM texture
and spectral features. The classification is repeated 10 times. Finally
the lithologic unit is determined by the voting method
and the results are statistically evaluated. The lithologic classification involves 92.1934% accuracy
exceeding the accuracy of spectral classification by 13.3369%
with a kappa coefficient of 0.9202. The multiscale texture extracts detailed lithologic information. The voting method prevents the dynamic lithologic change caused by the spatial variability of samples. The SVM also demonstrates superiority over the maximum likelihood classifier for lithologic classification involving high-dimensional and nonnormal distribution data. The local optimal parameters of SVM are avoided using the artificial bee colony algorithm to search for optimal parameters.
小波纹理投票法支持向量机ASTER岩性分类
wavelet texturevoting methodSupport Vector MachineASTERlithologic classification
Abrams M and Yamaguchi Y, 2019. Twenty years of ASTER contributions to lithologic mapping and mineral exploration. Remote Sensing, 11(11): 1394. [DOI: 10.3390/rs11111394http://dx.doi.org/10.3390/rs11111394]
Amer R and El-Desoky H. 2017. A remote sensing method for mapping sillimanite mineralization. Journal of African Earth Sciences, 134: 373-382 [DOI: 10.1016/j.jafrearsci.2017.07.008http://dx.doi.org/10.1016/j.jafrearsci.2017.07.008]
Gahlan H and Ghrefat H. 2018. Detection of gossan zones in Arid regions using Landsat 8 OLI data: implication for mineral exploration in the eastern Arabian shield, Saudi Arabia. Natural Resources Research, 27(1): 109-124 [DOI: 10.1007/s11053-017-9341-8http://dx.doi.org/10.1007/s11053-017-9341-8]
Hadigheh S M H and Ranjbar H. 2013. Lithological mapping in the eastern part of the central Iranian volcanic belt using combined ASTER and IRS data. Journal of the Indian Society of Remote Sensing, 41(4): 921-931
Hall-Beyer M. 2017. Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. International Journal of Remote Sensing, 38(5): 1312-1338 [DOI: 10.1080/01431161.2016.1278314http://dx.doi.org/10.1080/01431161.2016.1278314]
Harris J R, Grunsky E C, He J, Gorodetzky D and Brown N. 2012. A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics. Canadian Journal of Remote Sensing, 38(1): 69-90 [DOI: 10.5589/m12-013http://dx.doi.org/10.5589/m12-013]
He J, Harris J R, Sawada M and Behnia P. 2015. A comparison of classification algorithms using Landsat-7 and Landsat-8 data for mapping lithology in Canada’s Arctic. International Journal of Remote Sensing, 36(8): 2252-2276 [DOI: 10.1080/01431161.2015.1035410http://dx.doi.org/10.1080/01431161.2015.1035410]
Hosseinjani Zadeh M, Tangestani M H, Roldan F V and Yusta I. 2014. Sub-pixel mineral mapping of a porphyry copper belt using EO-1 Hyperion data. Advances in Space Research, 53(3): 440-451 [DOI: 10.1016/j.asr.2013.11.029http://dx.doi.org/10.1016/j.asr.2013.11.029]
Li Q Y, Huang X, Wen D W and Liu H. 2017. Integrating multiple textural features for remote sensing image change detection. Photogrammetric Engineering and Remote Sensing, 83(2): 109-121 [DOI: 10.14358/PERS.83.2.109http://dx.doi.org/10.14358/PERS.83.2.109]
Ma D and Lai H C. 2019. Remote sensing image matching based improved ORB in NSCT domain. Journal of the Indian Society of Remote Sensing, 47(5): 801-807 [DOI: 10.1007/s12524-019-00958-yhttp://dx.doi.org/10.1007/s12524-019-00958-y]
Masoumi F, Eslamkish T, Abkar A A, Honarmand M and Harris J R. 2017. Integration of spectral, thermal, and textural features of ASTER data using random forests classification for lithological mapping. Journal of African Earth Sciences, 129: 445-457 [DOI: 10.1016/j.jafrearsci.2017.01.028http://dx.doi.org/10.1016/j.jafrearsci.2017.01.028]
Moonon A U, Hu J W and Li S T. 2015. Remote sensing image fusion method based on nonsubsampled shearlet transform and sparse representation. Sensing and Imaging, 16(1): 23 [DOI: 10.1007/s11220-015-0125-0http://dx.doi.org/10.1007/s11220-015-0125-0]
Myint S W, Zhu T and Zheng B J. 2015. A novel image classification algorithm using overcomplete wavelet transforms. IEEE Geoscience and Remote Sensing Letters, 12(6): 1232-1236 [DOI: 10.1109/LGRS.2015.2390133http://dx.doi.org/10.1109/LGRS.2015.2390133]
Qin D D，Wan L，He P E，Zhang Y，Guo Y and Chen J. 2022. Multiscale object detection in remote sensing image by combining data fusion and feature selection. National Remote Sensing Bulletin，26（8）：1662-1673
秦登达，万里，何佩恩，张轶，郭亚，陈杰.2022.结合数据融合与特征选择的遥感影像尺度多样目标检测.遥感学报，26（8）：1662-1673 [DOI：10.11834/jrs.20221249http://dx.doi.org/10.11834/jrs.20221249]
Shu L S, Deng X L and Ma X X. 2019. Tectonic affinity between central Tianshan Basement and Tarim block craton. Earth Science, 44(5): 1584-1601
舒良树, 邓兴梁, 马绪宣. 2019. 中天山基底与塔里木克拉通的构造亲缘性. 地球科学, 44(5): 1584-1601 [DOI: 10.3799/dqkx.2019.977http://dx.doi.org/10.3799/dqkx.2019.977]
Singh A, Armstrong R T, Regenauer-Lieb K and Mostaghimi P. 2019. Rock characterization using Gray-Level Co-occurrence Matrix: an objective perspective of digital rock statistics. Water Resources Research, 55(3): 1912-1927 [DOI: 10.1029/2018WR023342http://dx.doi.org/10.1029/2018WR023342]
Wu K J, Zeng Y N, Jin W P, He L L and Li J. 2013. Remote sensing image classification based on improved ant-miner. Acta Geodaetica et Cartographica Sinica, 42(1): 59-66
吴孔江, 曾永年, 靳文凭, 何丽丽, 李静. 2013. 改进利用蚁群规则挖掘算法进行遥感影像分类. 测绘学报, 42(1): 59-66
Yu L, Porwal A, Holden E J and Dentith M C. 2012. Towards automatic lithological classification from remote sensing data using support vector machines. Computers and Geosciences, 45: 229-239 [DOI: 10.1016/j.cageo.2011.11.019http://dx.doi.org/10.1016/j.cageo.2011.11.019]
Yu Y F, Yang J Z, Chen S B and Wang N. 2015. Lithologic classification from remote sensing images based on spectral index. Earth Science—Journal of China University of Geosciences, 40(8): 1415-1419
于亚凤, 杨金中, 陈圣波, 王楠. 2015. 基于光谱指数的遥感影像岩性分类. 地球科学——中国地质大学学报, 40(8): 1415-1419 [DOI: 10.3799/dqkx.2015.127http://dx.doi.org/10.3799/dqkx.2015.127]
Zang S Y, Zhang C, Zhang L J and Zhang Y H. 2012. Wetland remote sensing classification using support vector machine optimized with genetic algorithm: a case study in Honghe nature national reserve. Scientia Geographica Sinica, 32(4): 434-441
臧淑英, 张策, 张丽娟, 张玉红. 2012. 遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例. 地理科学, 32(4): 434-441 [DOI: 10.13249/j.cnki.sgs.2012.04.006http://dx.doi.org/10.13249/j.cnki.sgs.2012.04.006]
Zhang C F, Yu J, Hao L N and Wang S J. 2017. Lithology extraction from synergies muti-scale texture and muti-spectra images. Geological Science and Technology Information, 36(4): 236-243
张翠芬, 余健, 郝利娜, 王少军. 2017. 多尺度纹理及多光谱影像协同的遥感岩性识别方法. 地质科技情报, 36(4): 236-243 [DOI: 10.19509/j.cnki.dzkq.2017.0431http://dx.doi.org/10.19509/j.cnki.dzkq.2017.0431]
Zhang R S, Cao H, Zeng M, Zhang D X and Li G X. 2016. Scientific belt lithologic mapping based on ASTER spectral analysis: A case study from the Tashkorgan area (Xinjiang, China). Acta Petrologica Sinica, 32(12): 3835-3846
张瑞丝, 曹汇, 曾敏, 张德贤, 李广旭. 2016. 基于ASTER光谱特征的科技廊带岩性填图: 以新疆塔什库尔干地区为例. 岩石学报, 32(12): 3835-3846
Zheng S, An Y F, Shi P L and Zhao T. 2021. Mapping the lithological features and ore-controlling structures related to Ni-Cu mineralization in the eastern Tian Shan, NW China from ASTER data. Remote Sensing, 13(2): 206 [DOI: 10.3390/rs13020206http://dx.doi.org/10.3390/rs13020206]
Zheng S and Fu B H. 2013. Lithological mapping of granitiods in the western Junggar from ASTER SWIR-TIR multispectral data: Case study in Karamay pluton, Xinjiang. Acta Petrologica Sinica, 29(8): 2936-2948
郑硕, 付碧宏. 2013. 基于ASTER SWIR-TIR多光谱数据的西准噶尔花岗岩类岩性信息提取与识别-以克拉玛依岩体为例. 岩石学报, 29(8): 2936-2948
Zhou G Q, Wang H Y, Sun Y, Shao Y and Yue T. 2019. Lithologic classification using multilevel spectral characteristics. Journal of Applied Remote Sensing, 13(1): 016513 [DOI: 10.1117/1.JRS.13.016513http://dx.doi.org/10.1117/1.JRS.13.016513]
Zhang J X，Gu H Y，Yang Y，Zhang H and Li H T. 2021. Research progress and trend of high-resolution remote sensing imagery intelligent interpretation. National Remote Sensing Bulletin，25（11）：2198-2210
张继贤，顾海燕，杨懿，张鹤，李海涛.2021 高分辨率遥感影像智能解译研究进展与趋势.遥感学报，25(11): 2198-2210 [DOI：10.11834/jrs.20210382http://dx.doi.org/10.11834/jrs.20210382]
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