Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information

Computers and Geosciences

Volumn 63, Issue , 2014, Pages 22-33

  Cracknell, Matthew J ^a   Reading, Anya M ^a  

^a UNIVERSITY OF TASMANIA   (Australia)

Author keywords

Geological mapping; Machine learning; Remote sensing; Spatial clustering; Spatial information; Supervised classification

Indexed keywords

COMPUTATIONALLY EFFICIENT; GEOLOGICAL MAPPING; LITHOLOGY CLASSIFICATION; MACHINE LEARNING ALGORITHM (MLAS); SENSITIVITY TO VARIATIONS; SPATIAL CLUSTERING; SPATIAL INFORMATIONS; SUPERVISED CLASSIFICATION;

DECISION TREES; FORECASTING; GEOLOGICAL SURVEYS; LEARNING SYSTEMS; LITHOLOGY; NEURAL NETWORKS; REMOTE SENSING; SENSITIVITY ANALYSIS; TOOLS;

LEARNING ALGORITHMS;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; COMPARATIVE STUDY; GEOLOGICAL MAPPING; LITHOLOGY; NEAREST NEIGHBOR ANALYSIS; REMOTE SENSING; SATELLITE DATA; SPATIAL ANALYSIS;

EID: 84888067055     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2013.10.008     Document Type: Article

References (56)

1. Anselin L. Local indicators of spatial association - LISA. Geogr. Anal. 1995, 27:93-115.
2. Breiman L. Bagging predictors. Machine Learn. 1996, 24:123-140.
3. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
4. Breiman L., Friedman J.H., Olshen R.A., Stone C.J. Classification and Regression Trees 1984, 358. Wadsworths & Brooks/Cole Advanced Books & Software, Pacific Grove, USA.
5. Buckley, P.M., Moriarty, T., Needham, J. (compilers), 2002. Broken Hill Geoscience Database, 2nd ed. Geological Survey of New SouthWales , Sydney.
6. Carneiro C.C., Fraser S.J., Croacutesta A.P., Silva A.M., Barros C.E.M. Semiautomated geologic mapping using self-organizing maps and airborne geophysics in the Brazilian Amazon. Geophysics 2012, 77:K17-K24.
7. Cohen J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20:37-46.
8. Congalton R.G., Green K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices 1998, 137. Lewis Publications, Boca Raton. first edn.
9. Cover T., Hart P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 1967, 13:21-27.
10. Cracknell M.J., Reading A.M. The upside of uncertainty: identification of lithology contact zones from airborne geophysics and satellite data using Random Forests and Support Vector Machines. Geophysics 2013, 78:WB113-WB126.
11. Fix E., Hodges J.L. Discriminatory analysis. Nonparametric discrimination; Consistency properties 1951, U.S. Air Force, School of Aviation Medicine, Randolph Field, Texas.
12. Foody G.M., Mathur A. A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42:1335-1343.
13. Gahegan M. On the application of inductive machine learning tools to geographical analysis. Geogr. Anal. 2000, 32:113-139.
14. Gelfort, R., 2006. On Classification of Logging Data, Department of Energy and Economics. Clausthal University of Technology, Germany, p. 131.
15. Getis A. Spatial autocorrelation. Handbook of Applied Spatial Analysis: Software, Tools, Methods and Applications 2010, 255-278. Springer-Verlag, Berlin. M.M. Fisher, A. Getis (Eds.).
16. Guyon I. Practical feature selection: from correlation to causality. Mining Massive Data Sets for Security - Advances in Data Mining, Search, Social Networks and Text Mining, and their Applications to Security 2008, 27-43. IOS Press, Amsterdam. F. Fogelman-Soulié, D. Perrotta, J. Piskorski, R. Steinberger (Eds.).
17. Guyon, I., 2009. A practical guide to model selection. In: Marie, J. (Ed.), Proceedings of the Machine Learning Summer School. Canberra, Australia, January 26 - February 6, Springer Text in Statistics, Springer p.37.
18. Ham J., Yangchi C., Crawford M.M., Ghosh J. Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2005, 43:492-501.
19. Hastie T., Tibshirani R., Friedman J.H. The elements of statistical learning: data mining. Inference and Prediction 2009, Springer, New York, USA, p. 533. 2nd edn.
20. Henery R.J. Classification. Machine Learning, Neural and Statistical Classification 1994, 6-16. Ellis Horwood, New York. D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.).
21. Hsu C.-W., Lin C.-J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 2002, 13:415-425.
22. Hsu C.-W., Chang C.-C., Lin C.-J. A Practical Guide to Support Vector Classification 2010, 16. Department of Computer Science, National Taiwan University, Taipei, Taiwan.
23. Huang C., Davis L.S., Townshend J.R.G. An assessment of support vector machines for land cover classification. Int. J. Remote Sens. 2002, 23:725-749.
24. Kanevski M., Pozdnoukhov A., Timonin V. Machine learning for spatial environmental data: theory. Applications and Software 2009, CRC Press, Boca Raton, USA, (368 pp.).
25. Karatzoglou A., Meyer D., Hornik K. Support vector machines in R. J. Stat. Softw. 2006, 15:28.
26. Kotsiantis S.B. Supervised machine learning: a review of classification techniques. Informatica 2007, 31:249-268.
27. Kovacevic, M., Bajat, B., Trivic, B., Pavlovic, R., 2009. Geological units classification of multispectral images by using Support Vector Machines. In: Proceedings of the International Conference on Intelligent Networking and Collaborative Systems, IEEE, pp. 267-272.
28. Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., 2012. caret: Classifcation and Regression Training, R Package Version 5.15-023.
29. Kuncheva L. Combining Pattern Classifiers: Methods and Algorithms 2004, 376. John Wiley & Sons.
30. Landgrebe T.C.W., Paclik P. The ROC skeleton for multiclass ROC estimation. Pattern Recogn. Lett. 2010, 31:949-958.
31. Leverington D.W. Discrimination of sedimentary lithologies using Hyperion and Landsat Thematic Mapper data: a case study at Melville Island, Canadian High Arctic. Int. J. Remote Sens. 2010, 31:233-260.
32. Leverington D.W., Moon W.M. Landsat-TM-based discrimination of lithological units associated with the Purtuniq Ophiolite, Quebec, Canada. Remote Sens. 2012, 4:1208-1231.
33. Li C.-H., Kuo B.-C., Lin C.-T., Huang C.-S. A spatial-contextual Support Vector Machine for remotely sensed image classification. IEEE Trans. Geosci. Remote Sens. 2012, 50:784-799.
34. Lloyd C.D. Local Models for Spatial Analysis 2011, 336. CRC Press, Taylor & Francis Group, Boco Raton, USA. 2nd edn.
35. Lu D., Weng Q. A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 2007, 28:823-870.
36. Marsland S. Machine Learning: An Algorithmic Perspective 2009, Chapman & Hall/CRC, (406 pp.).
37. Melgani F., Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42:1778-1790.
38. Molina R., PD́erez de la Blanca N., Taylor C.C. Modern statistical techniques. Machine Learning 1994, 29-49. Neural and Statistical Classification. Ellis Horwood, New York. D. Michie, D.J. Spiegelhalter, C.C. Taylor (Eds.).
39. Oommen T., Misra D., Twarakavi N.K.C., Prakash A., Sahoo B., Bandopadhyay S. An objective analysis of support vector machine based classification for remote sensing. Math. Geosci. 2008, 40:409-424.
40. Page R.W., Conor C.H.H., Stevens B.P.J., Gibson G.M., Preiss W.V., Southgate P.N. Correlation of Olary and Broken Hill Domains, Curnamona Province: possible relationship to Mount Isa and other North Australian Pb-Zn-Ag-bearing successions. Econ. Geol. 2005, 100:663-676.
41. Page R.W., Stevens B.P.J., Gibson G.M. Geochronology of the sequence hosting the Broken Hill Pb-Zn-Ag orebody, Australia. Econ. Geol. 2005, 100:633-661.
42. Pal M. Random forest classifier for remote sensing classification. Int. J. Remote Sens. 2005, 26:217-222.
43. Provost F., Fawcett T. Analysis and visualization of classifier performance: Comparison under imprecise class and cost distributions. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD 97) 1997, 43-48. American Association for Artificial Intelligence, Huntington Beach, CA.
44. Ripley B.D. Pattern Recognition and Neural Networks 1996, 403. Cambridge University Press, Cambridge, UK.
45. Rojas R. Neural Netwoks: A Systematic Introduction 1996, 502. Springer-Verlag, Berlin.
46. Song X., Duan Z., Jiang X. Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. Int. J. Remote Sens. 2012, 33:3301-3320.
47. Stevens B.P.J. Post-depositional history of the Willyama Supergroup in the Broken Hill Block, NSW. Aust. J. Earth Sci. 1986, 33:73-98.
48. Vapnik V.N. Statistical Learning Theory 1998, 736. John Wiley & Sons, Inc., New York, USA.
49. Venables W.N., Ripley B.D. Modern Applied Statistics with S 2002, Springer, New York, USA, (495 pp.). 4th edn.
50. Waske B., Braun M. Classifier ensembles for land cover mapping using multitemporal SAR imagery. ISPRS J. Photogramm. Remote Sens. 2009, 64:450-457.
51. Waske B., Benediktsson J.A., Árnason K., Sveinsson J.R. Mapping of hyperspectral AVIRIS data using machine-learning algorithms. Can. J. Remote Sens. 2009, 35:106-116.
52. Webster A.E. The Structural Evolution of the Broken Hill Pb-Zn-Ag deposit, New South Wales, Australia, ARC Centre for Excellence in Ore Deposit Research 2004, 430. University of Tasmania, Hobart.
53. Williams, D., 2009. Landsat 7 Science Data User's Handbook. National Aeronautics and Space Administration, Greenbelt, Maryland p. 186.
54. Willis I.L., Brown R.E., Stroud W.J., Stevens B.P.J. The early proterozoic Willyama supergroup: stratigraphic subdivision and interpretation of high to low-grade metamorphic rocks in the Broken Hill Block, New South Wales. J. Geol. Soc. Aust. 1983, 30:195-224.
55. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques 2005, 525. Elsevier/Morgan Kaufman, San Fransisco, USA. 2nd edn.
56. Yu L., Porwal A., Holden E.J., Dentith M.C. Towards automatic lithological classification from remote sensing data using support vector machines. Comput. Geosci. 2012, 45:229-239.