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唐淑兰，曹建农，王凯.2021.结合PCA、多尺度分割及SVM的ASTER遥感蚀变信息提取.遥感学报，25（2）： 653-664
Tang S L，Cao J N and Wang K. 2021. Remote sensing mineralization alteration information extraction based on PCA， Multilevel Segment Method， and SVM. National Remote Sensing Bulletin， 25（2）：653-664
为了利用遥感影像进行更加精确的找矿预测，本文选择新疆东天山尾亚地区ASTER数据进行矿化蚀变信息提取方法研究。为了提高信息提取精度，本文提出了结合主成分分析（PCA）、多尺度分割和支持向量机（SVM）的遥感矿化蚀变信息提取方法。首先，分析ASTER数据的特征，选取各矿化蚀变信息的特征波段，对组合波段进行主成分分析，获得主分量图像；然后，对各主分量图像进行多尺度分割，并获得分割之后的均值图像；接着，提取训练样本，利用SVM对训练样本进行训练，采用试验方法求得最优核参数和松弛变量，构造最优SVM模型；最后，运用最优SVM模型完成矿化蚀变信息的提取。进行主成分分析时，铁染蚀变信息选择Band 1、2、3、4组合，Al-OH基团蚀变信息选择Band 1、4、6、7组合，OH和CO
3
2-
基团蚀变信息采用Band 1、2、8、9组合。在运行SVM时采用了序列最小优化算法（SMO）进行求解，速度提高了12%。实验结果表明，与波段比值法、主成分分析法及基于光谱角和SVM的方法等3种方法相比，本文方法提取铁染蚀变信息、Al-OH基团蚀变信息及OH和CO
3
2-
基团蚀变信息的总体精度可达到87.98%、 90.01%及88.93%，Kappa系数分别为0.8011、0.8134及0.8023，与成矿区带、已知矿点和已有不同地质背景成矿特征相关性较好。
In order to accurately locate the deposit
the ASTER data of Weiya area in eastern Tianshan Mountain of Xinjiang is selected to study the extraction method of mineralization alteration information. To improve the accuracy of ASTER data mineralization alteration information extraction method
a method based on Principal Component Analysis (PCA)
multilevel segment method
and Support Vector Machine (SVM) is proposed in this study. First
the special band of alteration information is selected after analyzing the ASTER data
and the principal component image is acquired by PCA. Then
the mean image is obtained after the principal component image is segmented. Subsequently
the training samples are trained by SVM after the training samples are extracted. Moreover
the optimal model is constructed using the optimal kernel parameters and flabby variable obtained by repeated testing. Finally
the optimal model is used to accomplish the extraction of alteration information from ASTER data. The abnormal ferric contamination is extracted using 1
2
3
and 4 bands
the alteration anomalies with AL-OH groups are extracted from 1
4
6
and 7 bands
and the alteration anomalies with OH
CO
3
2-
groups are extracted by 1
2
8
and 9 bands. SMO is adopted to improve operation efficiency. Thus
the speed is increased by 12%. A comparison with band ratio method
PCA method
spectral angle mapper and SVM method is conducted. The degree of the abnormal ferric contamination
the alteration anomalies with AL-OH groups
and the alteration anomalies with OH and CO
3
2-
groups are 87.98%
90.01%
and 88.93%
respectively. The corresponding Kappa coefficients are 0.8011
0.8134
and 0.8023. The extraction results of anomaly information are consistent with metallogenic belt
the known mineralization points
and the mineralization characteristics of different geological conditions.
遥感ASTER矿化蚀变信息提取多尺度分割主成分分析（PCA）支持向量机（SVM）序列最小优化算法（SMO）
remote sensingASTERmineralization alteration information extractionmultilevel segment methodPrincipal Component Analysis (PCA)Support Vector Machine(SVM)Sequential Minimum Optimization(SMO)
Amer R, Kusky T and Ghulam A. 2010. Lithological mapping in the Central Eastern Desert of Egypt using ASTER data. Journal of African Earth Sciences, 56(2/3): 75-82 [DOI: 10.1016/j.jafrearsci.2009.06.004http://dx.doi.org/10.1016/j.jafrearsci.2009.06.004]
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]
Georgescu M, Moustaoui M, Mahalov A and Dudhia J. 2013. Summer-time climate impacts of projected megapolitan expansion in Arizona. Nature Climate Change, 3(1): 37-41 [DOI: 10.1038/nclimate1656http://dx.doi.org/10.1038/nclimate1656]
Hosseinjani Zadeh M, Tangestani M H, Roldan F V and Yusta I. 2014a. 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]
Hosseinjani Zadeh M, Tangestani M H, Roldan F V and Yusta I. 2014b. Mineral exploration and alteration zone mapping using mixture tuned matched filtering approach on ASTER data at the central part of Dehaj-Sarduiyeh copper belt, SE Iran. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 284-289 [DOI: 10.1109/JSTARS.2013.2261800http://dx.doi.org/10.1109/JSTARS.2013.2261800]
Huang X and Zhang L P. 2013. An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 257-272 [DOI: 10.1109/TGRS.2012.2202912http://dx.doi.org/10.1109/TGRS.2012.2202912]
Jiang Y T. 2019. Research on road extraction of remote sensing image based on convolutional neural network. EURASIP Journal on Image and Video Processing, 2019: 31 [DOI: 10.1186/s13640-019-0426-7http://dx.doi.org/10.1186/s13640-019-0426-7]
Johnson B and Xie Z X. 2011. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4): 473-483 [DOI: 10.1016/j.isprsjprs.2011.02.006http://dx.doi.org/10.1016/j.isprsjprs.2011.02.006]
Karaboga D and Ozturk C. 2011. A novel clustering approach: artificial bee colony (ABC) algorithm. Applied Soft Computing, 11(1): 652-657 [DOI: 10.1016/j.asoc.2009.12.025http://dx.doi.org/10.1016/j.asoc.2009.12.025]
Kaur S, Bansal R K, Mittal M, Goyal L M, Kaur I, Verma A and Son L H. 2019. Mixed pixel decomposition based on extended fuzzy clustering for single spectral value remote sensing images. Journal of the Indian Society of Remote Sensing, 47(3): 427-437 [DOI: 10.1007/s12524-019-00946-2http://dx.doi.org/10.1007/s12524-019-00946-2]
Koda S, Zeggada A, Melgani F and Nishii R. 2018. Spatial and structured SVM for multilabel image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(10): 5948-5960 [DOI: 10.1109/TGRS.2018.2828862http://dx.doi.org/10.1109/TGRS.2018.2828862]
Liu C, Hong L, Chen J, Chu S S and Deng M. 2015. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image. Journal of Remote Sensing, 19(2): 228-238
刘纯, 洪亮, 陈杰, 楚森森, 邓敏. 2015. 融合像素—多尺度区域特征的高分辨率遥感影像分类算法. 遥感学报, 19(2): 228-238 [DOI: 10.11834/jrs.20154035http://dx.doi.org/10.11834/jrs.20154035]
Liu J Y, Chen L, Li W, Wang G H and Wang B. 2019. An improved method for extracting alteration related to the ductile shear zone type gold deposits using ASTER data. Remote Sensing for Land and Resources, 31(1): 229-236
刘建宇, 陈玲, 李伟, 王根厚, 王博. 2019. 基于ASTER数据韧性剪切带型金矿蚀变信息提取方法优化. 国土资源遥感, 31(1): 229-236 [DOI: 10.6046/gtzyyg.2019.01.30http://dx.doi.org/10.6046/gtzyyg.2019.01.30]
Mohy H, El-Magd I A and Basta F F. 2016. Newly improved band ratio of ASTER data for lithological mapping of the Fawakhir Area, central eastern desert, Egypt. Journal of the Indian Society of Remote Sensing, 44(5): 735-746 [DOI: 10.1007/s12524-015-0539-0http://dx.doi.org/10.1007/s12524-015-0539-0]
Pour A B, Hashim M and Van Genderen J. 2013. Detection of hydrothermal alteration zones in a tropical region using satellite remote sensing data: bau goldfield, Sarawak, Malaysia. Ore Geology Reviews, 54: 181-196 [DOI: 10.1016/j.oregeorev.2013.03.010http://dx.doi.org/10.1016/j.oregeorev.2013.03.010]
Sadeghi B, Khalajmasoumi M, Afzal P, Moarefvand P, Yasrebi A B, Wetherelt A, Foster P and Ziazarifi A. 2013. Using ETM+ and ASTER sensors to identify iron occurrences in the Esfordi 1:100,000 mapping sheet of Central Iran. Journal of African Earth Sciences, 85: 103-114 [DOI: 10.1016/j.jafrearsci.2013.05.003http://dx.doi.org/10.1016/j.jafrearsci.2013.05.003]
Sharif I and Chaudhuri D. 2019. A multiseed-based SVM classification technique for training sample reduction. Turkish Journal of Electrical Engineering and Computer Sciences, 27(1): 595-604 [DOI: 10.3906/elk-1801-157http://dx.doi.org/10.3906/elk-1801-157]
Song C M, Gallos L K, Havlin S and Makse H A. 2007. How to calculate the fractal dimension of a complex network: the box covering algorithm. Journal of Statistical Mechanics: Theory and Experiment, 2007(3): P03006 [DOI: 10.1088/1742-5468/2007/03/P03006http://dx.doi.org/10.1088/1742-5468/2007/03/P03006]
Sun W W, Liu C, Li J L, Lai Y M and Li W Y. 2014. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery. Journal of Applied Remote Sensing, 8(1): 083641 [DOI: 10.1117/1.JRS.8.083641http://dx.doi.org/10.1117/1.JRS.8.083641]
Tang Y, Wang L J, Ma G C, Jia H J and Jin X. 2019. Emergency monitoring of high-level landslide disasters in Jinsha River using domestic remote sensing satellites. Journal of Remote Sensing, 23(2):252-261
唐尧， 王立娟， 马国超， 贾虎军， 靳晓. 2019. 利用国产卫星进行金沙江高位滑坡灾害灾情应急监测. 遥感学报， 23(2): 252-261 [DOI: 10.11834/jrs.20198405http://dx.doi.org/10.11834/jrs.20198405]
Wang Q and Chen J P. 2009. Extraction and grading of remote sensing alteration anomaly based on the fractal theory. Geological Bulletin of China, 28(2/3): 285-288
王倩, 陈建平. 2009. 基于分形理论的遥感蚀变异常提取和分级. 地质通报, 28(2/3): 285-288 [DOI: 10.3969/j.issn.1671-2552.2009.02.022http://dx.doi.org/10.3969/j.issn.1671-2552.2009.02.022]
Wu Y Q, Sheng D H and Zhou Y. 2018. Remote sensing mineralization alteration information extraction based on PCA and SVM optimized by cuckoo algorithm. Journal of Remote Sensing, 22(5): 810-821
吴一全, 盛东慧, 周扬. 2018. PCA和布谷鸟算法优化SVM的遥感矿化蚀变信息提取. 遥感学报, 22(5): 810-821 [DOI: 10.11834/jrs.20187068http://dx.doi.org/10.11834/jrs.20187068]
Wu Z C, Ye F W, Guo F S, Liu W H, Li H L and Yang Y. 2018. A review on application of techniques of principle component analysis on extracting alteration information of remote sensing. Journal of Geo-information Science, 20(11): 1644-1656
吴志春, 叶发旺, 郭福生, 刘文恒, 李华亮, 杨羿. 2018. 主成分分析技术在遥感蚀变信息提取中的应用研究综述. 地球信息科学学报, 20(11): 1644-1656 [DOI: 10.12082/dqxxkx.2018.180195http://dx.doi.org/10.12082/dqxxkx.2018.180195]
Xu R, Lin Q Z and Chen Y. 2015. Fractal theory based multi-scale features analysis on alteration minerals. Science of Surveying and Mapping, 40(9): 138-142
徐茹, 蔺启忠, 陈玉. 2015. 基于分形方法的蚀变矿物多尺度特征分析. 测绘科学, 40(9): 138-142 [DOI: 10.16251/j.cnki.1009-2307.2015.09.029http://dx.doi.org/10.16251/j.cnki.1009-2307.2015.09.029]
Yan J N, Zhou K F, Wang J L, Wang S S, Wang W and Li D. 2013. Extraction of hyper-spectral remote sensing alteration information based on SAM and SVM. Computer Engineering and Applications, 49(19): 141-146
阎继宁, 周可法, 王金林, 王珊珊, 汪玮, 李东. 2013. 基于SAM与SVM的高光谱遥感蚀变信息提取. 计算机工程与应用, 49(19): 141-146 [DOI: 10.3778/j.issn.1002-8331.1301-0067http://dx.doi.org/10.3778/j.issn.1002-8331.1301-0067]
Yan X J, Gong R X and Zhang Q F. 2016. Application of optimization SVM based on improved genetic algorithm in short-term wind speed prediction. Power System Protection and Control, 44(9): 38-42
颜晓娟, 龚仁喜, 张千锋. 2016. 优化遗传算法寻优的SVM在短期风速预测中的应用. 电力系统保护与控制, 44(9): 38-42 [DOI: 10.7667/PSPC150294http://dx.doi.org/10.7667/PSPC150294]
Yang B, Li M J, Wang S J, Gao G S, He Z P and Wang Z. 2015. Anomaly information extraction of mineralization alteration in Taxkorgan with ASTER. Remote Sensing Information, 30(4): 109-114
杨斌, 李茂娇, 王世举, 高桂胜, 何兆培, 汪峥. 2015. ASTER数据在塔什库尔干地区矿化蚀变信息的提取. 遥感信息, 30(4): 109-114 [DOI: 10.3969/j.issn.1000-3177.2015.04.019http://dx.doi.org/10.3969/j.issn.1000-3177.2015.04.019]
Yokoya N, Yairi T and Iwasaki A. 2012. Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 528-537 [DOI: 10.1109/TGRS.2011.2161320http://dx.doi.org/10.1109/TGRS.2011.2161320]
Yu Y, Li J G, Chen S B, Gao X S, Lu P, Huang S and Zhang C X. 2015. ASTER image alteration minerals information extraction based on different lithology backgrounds. Earth Science (Journal of China University of Geosciences), 40(8): 1391-1395
于岩, 李建国, 陈圣波, 高学生, 路鹏, 黄爽, 张晨曦. 2015. 基于不同岩性背景的遥感影像蚀变矿物信息提取. 地球科学——中国地质大学学报, 40(8): 1391-1395 [DOI: 10.3799/dqkx.2015.123http://dx.doi.org/10.3799/dqkx.2015.123]
Zhang X Y and Li P J. 2014. Lithological mapping from hyperspectral data by improved use of spectral angle mapper. International Journal of Applied Earth Observation and Geoinformation, 31: 95-109 [DOI: 10.1016/j.jag.2014.03.007http://dx.doi.org/10.1016/j.jag.2014.03.007]
Zhao H, Li X F, Zhu H B and Wang B. 2018. Shape modeling of pulmonary nodules based on fractal dimension characteristic. Journal of Northeastern University (Natural Science), 39(11): 1545-1550, 1555
赵海, 李雄峰, 朱宏博, 王彬. 2018. 基于分形维数特征的肺结节形状建. 东北大学学报(自然科学版), 39(11): 1545-1550, 1555 [DOI: 10.12068/j.issn.1005-3026.2018.11.006http://dx.doi.org/10.12068/j.issn.1005-3026.2018.11.006]
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