Separating alteration units in the Takht-e-Gonbad district using via comparing two classification methods of Support vector machine and maximum likelihood,
Subject Areas :Davoud Nazari 1 , neda mahvash mohammadi 2 , Adabi 3 , 4 , Mohammad Ghavidel-Syooki 5 , haniyeh kalani 6
1 -
2 -
3 -
4 -
5 -
6 -
Keywords: Alteration Support vector machine Maximum likelihood, Remote sensing ASTER. ,
Abstract :
Separation of alteration units has an important role in exploration of ore deposits. In the past, classical methods were used for this purpose. Recently, the support vector machine (SVM), one of the most important data driven models, has been applied for geological purpose. This algorithm is a useful learning system based on constrained optimization theory. In this study, the SVM algorithm with various kernels and maximum likelihood method were used to separate the alteration units of the Takht-e-Gonbad district situated in Chahar Gonbad sheet by using satellite images of the ASTER sensor. The results were analyzed and evaluated according to the field studies. Based on the achieved results and field studies, the SVM method with the RBF kernel function compared to other kernels and the maximum likelihood method had the highest accuracy (89.17%) and kappa coefficient (0.83). Thus, the SVM method for classification of alteration is more accurate compared to other discussed methods.
- حسینی، م.ر.، قادری، م. و علیرضایی، س. 1389. انواع سیستم¬های رگه- رگچه و ارتباط آن¬ها با کانه¬زایی در کانسار مس تخت گنبد، شمال¬شرق سیرجان، پانزدهمین همایش انجمن زمین¬شناسی ایران، دانشگاه تربیت‌معلم، تهران، 245-252.
- خان ناظر، ن.ح.، 1374. نقشه زمين¬شناسي 1:100000 چهارگنبد، سازمان زمین‌شناسی كشور.
- گزارش زمین‌شناسی كانسار تخت گنبد،1389. شركت مس تخت گنبد سیرجان.
- گودرزي مهر، س.، عباسپور، ر.ع.، احدنژاد، و.، و خاکباز، ب.، 1391. مقايسه روش ماشين بردار پشتيبان با روش¬هاي حداکثر احتمال و شبکه عصبي براي تفکيک واحدهاي سنگ¬شناسی، فصلنامه زمين¬شناسي ايران، 22، ۷۵-۹۲.
- لگ، ک.، 1380. ترجمه فرهت جاه، دورسنجی و سیستم اطلاعات جغرافیایی، نیروهای مسلح، تهران.
- ماهوش محمدی، ن.، هزارخانی، الف. و مقصودی، ع.، 1396. به‌کارگیری روش¬هاي مختلف پردازش تصاوير ماهواره¬اي به‌منظور شناسايي و تفکيک بخش¬هاي دگرساني منطقه خوني و کالکافي(استان اصفهان)، مجله پژوهش¬های دانش زمین، 33، 137-152.
- مختاری، م. و نجفی، ا.، 1394. مقايسه روش¬هاي طبقه¬بندي ماشين بردار پشتيبان و شبکه عصبي مصنوعي در استخراج کاربري¬هاي اراضي از تصاوير ماهواره¬اي لندست TM. مجله علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، 72، 35-44.
- ندوشن، م.، سفیانیان، ع. و خواجه الدین، س.ج.، 1388. تهيه نقشه پوشش اراضي شهر اراك با استفاده از روش¬هاي طبقه‌بندی شبكه عصبي مصنوعي و حداكثر احتمال، پژوهش¬های جغرافیای طبیعی، 69، 83-98.
- Abbaszadeh, M., Hezarkhani, A. and Soltani-mohammadi, S., 2013. An SVM-based machine learning method for the separation of alteration zones in Sungun porphyry copper deposit, Chemie der Erde-Geochemistry. 73,4, 545–554.
- Abedi, M., Norouzi, G. H. and Bahroudi, A. 2012. Support vector machine for multi-classification of mineral prospectivity areas. Computers and Geosciences. 46, 272–283. https://doi.org/10.1016/j.cageo.2011.12.014.
-Basak, D., Pal, S. and Patranabis, D.C., 2007. Support vector regression. Neural Inf. Process. 11, 203-225.
- Campbell, J.B., 1996. Introduction to Remote Sensing. 2th edition, New York: The Guilford Press. 622.
- Campbell, J.B. and Wynne, R.H., 2011. Introduction to remote sensing. Guilford Press. 724.
-Camps-Valls, G., Mooij, J. and Scholkopf, B., 2010. Remote sensing feature selection by kernel dependence measures. IEEE Geoscience and Remote Sensing Letters. 7, 587–591.
- Genderen, V.J.L. and Lock, B.F., 1978, Remote sensing: statistical testing of thematic map accuracy. Remote Sensing of Environment. 7, 3–14.
-Granian, H., Tabatabaei, S.H., Asadi, H.H. and Carranza, E.J., 2016. Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit, NW Iran. Nat. Resour. Res. 25,2,145–159.
- Guo, Y., De Jong, K., Liu, F., Wang, X. and Li, C., 2012. A Comparison of Artificial Neural Networks and Support Vector Machines on Land Cover Classification, Springer-verlag Berlin Heidelberg, ISICA, CCIS. 316, 531-539.
- Hixon, R.F., Solis Ramirez, M.J. and Villoch, M., 1981. Aspects of morphometric and reproduction of the squid Ommastrephes petrous, Steenstrup 1885 in the western Gulf of Mexico. American Malacological Bulletin Union, 54-60.
-Hastie, T., Tibshirani, R. and Friedman, J., 2009. Overview of supervised learning. In The elements of statistical learning. Springer New York. 9-41.
-Hord, R.M., 1982. Digital Image Processing of Remotely Sensed Data, Academic Press, New York, 256.
-Huang, C., Davis, L.S. and Townshend, J.R.G., 2002. An assessment of support vector machines for land cover classification. International Journal of Remote Sensing. 23, 725–749.
- Jensen, J.R., 2005. Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd Edition, Upper Saddle River: Prentice-Hall. 526.
-Jiang, X., Lin, M. And Zhao, C., 2011. Woodland cover change assessment using decision trees,support vector machines and artificial neural networks classification algorithms, Fourth International Conferenceon Intelligent Computation Technology and Automation. 312-315.
- Kavzoglu, T. and Colkesen, I., 2009. A kernel functions analysis for support vector machines for land cover classification, Int. J. of Applied Earth Observation and Geoinformation. 11, 352-359.
-Liu, G.Q., 2011. Comparison of Regression and ARIMA models with Neural Network models to forecast the daily streamflow. PhD thesis, University of Delaware. 545.
-Mantero, P., Moser, G. and Serpico, S.B., 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Trans. on Geoscience and Remote Sensing. 43, 559-570.
-Mountrakis, G., Im, J. and Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing. 13, 247–259.
-Oommen, T., 2008. An objective analysis of Support Vector Machine based classification for remote sensing. Mathematical Geosciences. 40, 409–424.
-Pal, M. and Mather, P.M., 2005. Support vector machines for classification in remote sensing, International Journal of Remote Sensing. 26,5, 1007-1011.
-Schrader, S. and Pouncy, R., 1997. ERDAS Field Guide. 4th ed. ERDAS, Inc. Atlanta. 825.
- Shabankareh, M. and Hezarkhani, A., 2016. Journal of African Earth Sciences Application of support vector machines for copper potential mapping in Kerman region, Iran, Journal of African Earth Sciences. 25, 102-112.
-Shafiei, B., 2010. Lead isotope signatures of the igneous rocks and porphyry copper deposits from the Kerman Cenozoic magmatic arc (SE Iran), and their magmatic-metallogenetic implications, Ore Geology Reviews. 38, 27-36.
-Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. Springer-Verlag, New York.
-Yu, L., Porwal, A., Holden, E. and Dentith, M.C., 2012. Computers & Geosciences Towards automatic lithological classification from remote sensing data using support vector machines. 45, 229–239.
-Zhang, X., Pazner, M. and Duke, N., 2007. Lithologic and mineral information extraction for gold exploration using ASTER data in the south Chocolate Mountains (California). ISPRS Journal of Photogrammetry and Remote Sensing, 62,4, 271-282.
- Zuo, R. and Carranza, E.J.M., 2011. Support vector machine: A tool for mapping mineral prospectivity. Comput. Geosci. 37, 12, 1967–1975.
