Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis

European Journal of Remote Sensing

Volumn 48, Issue , 2015, Pages 403-422

  Ustuner, Mustafa ^a   Sanli, Fusun Balik ^a   Dixon, Barnali ^b  

^a YILDIZ TECHNICAL UNIVERSITY   (Turkey)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Kernel; Landuse classification; RapidEye; Sensitivity; Support Vector Machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); FUNCTIONS; IMAGE CLASSIFICATION; LEARNING SYSTEMS; MAXIMUM LIKELIHOOD; POLYNOMIALS; SENSITIVITY ANALYSIS;

CLASSIFICATION ALGORITHM; KERNEL; LANDUSE CLASSIFICATIONS; MAXIMUM LIKELIHOOD CLASSIFICATIONS; RAPIDEYE; SENSITIVITY; STATISTICAL LEARNING METHODS; SUPPORT VECTOR MACHINE (SVMS);

SUPPORT VECTOR MACHINES;

ACCURACY ASSESSMENT; ALGORITHM; IMAGE CLASSIFICATION; LAND CLASSIFICATION; LAND USE CHANGE; LEARNING; MAXIMUM LIKELIHOOD ANALYSIS; REMOTE SENSING; SATELLITE IMAGERY; SENSITIVITY ANALYSIS; SUPPORT VECTOR MACHINE;

TURKEY;

EID: 84947218144     PISSN: None     EISSN: 22797254     Source Type: Journal    
DOI: 10.5721/EuJRS20154823     Document Type: Article

References (42)

1. Abdikan S., Bilgin G., Sanli F.B., Uslu E., Ustuner M. (2015) - Enhancing land use classification with fusing dual-polarized TerraSAR-X and multispectral RapidEye data. Journal of Applied Remote Sensing, 9: 096054-096054. doi: http://dx.doi.org/10.1117/1.JRS.9.096054.
2. Adam E., Mutanga O., Odindi J., Abdel-Rahman E.M. (2014) - Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35: 3440-3458. doi: http://dx.doi.org/10.1080/01431161.2014.903435.
3. Adelabu S., Mutanga O., Adam E. (2014) - Evaluating the impact of red-edge band from Rapideye image for classifying insect defoliation levels. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 34-41. doi: http://dx.doi.org/10.1016/j.isprsjprs.2014.05.013.
4. Agricultural Research and Extension Council of Alberta (2010) - Precision Farming and Variable Rate Technology. Available online at: http://areca.ab.ca/userfiles/files/VRT_Resource_Manual_Mar_2010.pdf. (last accessed 30.04.2014).
5. BlackBridge (2013) - RapidEye Satellite Imagery Product Specifications, Version 6. Available online at: http://www.blackbridge.com/rapideye/upload/RE_Product_Specifications_ENG.pdf.
6. Cortes C., Vapnik V. (1995) - Support-Vector Networks. Machine Learning, 20: 273-297. doi: http://dx.doi.org/10.1007/BF00994018.
7. Congalton R.G. (1991) - A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37: 35-46. doi: http://dx.doi.org/10.1016/0034-4257(91)90048-B.
8. Conrad C., Dech S., Dubovyk O., Fritsch S., Klein D., Löw F., Schorcht G., Zeidler J. (2014) - Derivation of temporal windows for accurate crop discrimination in heterogeneous croplands of Uzbekistan using multitemporal RapidEye images. Computers and Electronics in Agriculture, 103: 63-74. di: http://dx.doi.org/10.1016/j.compag.2014.02.003.
9. Dixon B., Candade N. (2007) - Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29: 1185-1206. doi: http://dx.doi.org/10.1080/01431160701294661.
10. ENVI (2006) - Online Help, Applying Support Vector Machine Classification. Avalable online at: http://gridkr.com/d/ENVI_4_3/online_help/ApplyingSVMClassification.html (last accessed: 30.04.2014)
11. European Environment Agency (2010) - The European environment - state and outlook 2010. Available online at: http://www.eea.europa.eu/soer/countries/tr/soertopic_view?topic=land (last accessed: 30.04.2014).
12. Forkuor G., Conrad C., Thiel M., Ullmann T., Zoungrana E. (2014) - Integration of Optical and Synthetic Aperture Radar Imager for Improving Crop Mapping in Northwestern Benin, West Africa. Remote Sensing, 6: 6472-6499. doi: http://dx.doi.org/10.3390/rs6076472.
13. Hall F.G., Townshend J.R., Engman E.T. (1995) - Status of remote sensing algorithms for estimation of land surface state parameters. Remote Sensing of Environment, 51: 138-156. doi: http://dx.doi.org/10.1016/0034-4257(94)00071-T.
14. Huang C., Davis L.S., Townshend J.R.G. (2002) - An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23: 725-749. doi: http://dx.doi.org/10.1080/01431160110040323.
15. Weeber J. (2010) - RapidEye Satellite Imaging System, Defense/Intelligence Capabilities. Available online at: http://usgif.org/system/uploads/1009/original/John_Weeber.pdf (last accesseded: 27.08.2014).
16. Kavzoglu T., Colkesen I. (2009) - A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11: 352-359. doi: http://dx.doi.org/10.1016/j.jag.2009.06.002.
17. Lorup E.J. (1996) - Crosstabulation. Avilable online at: http://uhaweb.hartford.edu/gatetutor/idrisi/mptools2.html (last accesseded: 30.04.2014).
18. Löw F., Michel U., Dech S., Conrad C. (2013) - Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing, 85: 102-119. doi: http://dx.doi.org/10.1016/j.isprsjprs.2013.08.007.
19. Löw F., Knöfel P., Conrad C. (2015) - Analysis of uncertainty in multi-temporal object-based classification. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 91-106. doi: http://dx.doi.org/10.1016/j.isprsjprs.2015.03.004.
20. Mather P.M. (2001) - Computer processing of Remotely-Sensed images: An Introduction. New York: John Wiley & Sons.
21. Mathur A., Foody G.M. (2008) - Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29: 2227-2240. doi: http://dx.doi.org/10.1080/01431160701395203.
22. McNairn H., Ellis J., Van Der Sanden J.J., Hirose T., Brown R.J. (2002) - Providing crop information using RADARSAT-1 and satellite optical imagery. International Journal of Remote Sensing, 23: 851-870. doi: http://dx.doi.org/10.1080/01431160110070753.
23. Mountrakis G., Im J., Ogole C. (2011) - Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66: 247-259. doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001.
24. Otukei J.R., Blaschke T. (2010) - Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12 (1): S27-S31. doi: http://dx.doi.org/10.1016/j.jag.2009.11.002.
25. Pal M., Mather P.M. (2004) - Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20: 1215-1225. doi: http://dx.doi.org/10.1016/j.future.2003.11.011.
26. Pal M., Mather P.M. (2005) - Support vector machines for classification in remote sensing. International Journal of Remote Sensing, 26: 1007-1011. doi: http://dx.doi.org/10.1080/01431160512331314083.
27. Pal M. (2012) - Advanced algorithms for land use and cover classification. Advances in Mapping from Remote Sensor Imagery, CRC Press, pp. 69-90. doi: http://dx.doi.org/10.1201/b13770-4.
28. Petropoulos G.P., Kalaitzidis C., Prasad Vadrevu K. (2012) - Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41: 99-107. doi: http://dx.doi.org/10.1016/j.cageo.2011.08.019.
29. Rogan J., Chen D. (2004) - Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61: 301-325. doi: http://dx.doi.org/10.1016/S0305-9006(03)00066-7.
30. Roscher R., Förstner W., Waske B. (2012) - I2VM: Incremental import vector machines. Image and Vision Computing, 30: 263-278. doi: http://dx.doi.org/10.1016/j.imavis.2012.04.004.
31. Sandau R. (2010) - Status and trends of small satellite missions for Earth observation. Acta Astronautica, 66: 1-12. doi: http://dx.doi.org/10.1016/j.actaastro.2009.06.008.
32. Schuster C., Förster M., Kleinschmit B. (2012) - Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing, 33: 5583-5599. doi: http://dx.doi.org/10.1080/01431161.2012.666812.
33. Schuster C., Schmidt T., Conrad C., Kleinschmit B., Förster M. (2015) - Grassland habitat mapping by intra-annual time series analysis - Comparison of RapidEye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation, 34: 25-34. doi: http://dx.doi.org/10.1016/j.jag.2014.06.004.
34. Soria-Ruiz J., Fernandez-Ordonez Y., McNairn H. (2009) - Corn Monitoring and Crop Yield Using Optical and Microwave Remote Sensing. In Ho P-G. P. (Ed.), Geoscience and Remote Sensing, InTech, Open Access Publisher, Chapter 19. doi: http://dx.doi.org/10.5772/8311.
35. Tigges J., Lakes T., Hostert P. (2013) - Urban vegetation classification: Benefits of multitemporal RapidEye satellite data. Remote Sensing of Environment, 136: 66-75. doi: http://dx.doi.org/10.1016/j.rse.2013.05.001.
36. Townshend J.R.G. (1992) - Land cover. International Journal of Remote Sensing, 13: 1319-1328. doi: http://dx.doi.org/10.1080/01431169208904193.
37. Tyc G., Tulip J., Schulten D., Krischke M., Oxfort M. (2005) - The RapidEye mission design. Acta Astronautica, 56: 213-219. doi: http://dx.doi.org/10.1016/j.actaastro.2004.09.029.
38. Unal E., Mermer A., Urla O., Ceylan N., Yildiz H., Aydogdu M. (2006) - Pilot study on wheat area estimates in Turkey. ISPRS WG VIII/10 Workshop 2006: 135-138.
39. Vapnik V.N. (1995) - The nature of statistical learning theory. Springer, New York. doi: http://dx.doi.org/10.1007/978-1-4757-2440-0.
40. Visser H., de Nijs T. (2006) - The Map Comparison Kit. Environmental Modelling & Software, 21: 346-358. doi: http://dx.doi.org/10.1016/j.envsoft.2004.11.013.
41. Watanachaturaporn P., Arora M.K., Varshney P.K. (2008) - Multisource classification using support vector machines: an empirical comparison with decision tree and neural network classifiers. Photogrammetric Engineering & Remote Sensing, 74 (2): 239-246. doi: http://dx.doi.org/10.14358/PERS.74.2.239.
42. Yang X. (2011) - Parameterizing Support Vector Machines for Land Cover Classification. Photogrammetric Engineering & Remote Sensing, 77: 27-37. doi: http://dx.doi.org/10.14358/PERS.77.1.27.