Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in Southeastern Turkey

Photogrammetric Engineering and Remote Sensing

Volumn 79, Issue 11, 2013, Pages 1053-1065

  Alganci, Ugur ^a   Sertel, Elif ^a   Ozdogan, Mutlu ^b   Ormeci, Cankut ^a  

^a ISTANBUL TECHNICAL UNIVERSITY   (Turkey)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION ALGORITHM; MULTI-RESOLUTION IMAGERY; OBJECT-BASED IMAGE CLASSIFICATION; PIXEL- AND OBJECT-BASED CLASSIFICATION; PIXEL-BASED METHODS; POST CLASSIFICATION; SOUTHEASTERN TURKEY; SPECTRAL ANGLE MAPPERS;

ALGORITHMS; ESTIMATION; IMAGE CLASSIFICATION; IMAGE RESOLUTION; PIXELS; SUPPORT VECTOR MACHINES;

CROPS;

ALGORITHM; DATA ACQUISITION; HETEROGENEITY; IMAGE CLASSIFICATION; IMAGE RESOLUTION; RESEARCH; SPATIAL DISTRIBUTION; SPATIAL RESOLUTION;

TURKEY;

EID: 84887290816     PISSN: 00991112     EISSN: None     Source Type: Journal    
DOI: 10.14358/PERS.79.11.1053     Document Type: Article

References (45)

1. Astrium Website, 2013. Technical Specification of the SPOT Satellites, URL: http://www.astrium-geo.com/en/4388-spot-1-to-spot-5-satellite-images (last date accessed: 15 August 2013).
2. Beach, R.H., B.J. DeAngelo, S.K. Rose, C. Li, W. Salas, and S.J. Del Grosso, 2008. Mitigation potential and costs for global agricultural greenhouse gas emissions, Agricultural Economics, 38:1-7.
3. Benz, U.C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, 2004. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS ready information, ISPRS Journal of Photo-grammetry and Remote Sensing, 58:239-258
4. Bhaskaran, S., S. Paramananda, and M. Ramnarayan, 2010. Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data, Applied Geography, 30: 650-665.
5. Blaschke, T., 2010. Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 66(1): 2-16.
6. Caprioli, M., and E. Tarantino, 2001. Accuracy assessment of per-field classification integrating very fine spatial resolution satellite imagery with topographic data, Journal of Geospatial Engineering, 3(2):127-134.
7. Castillejo-González, I.L., F. López-Granados, A. García-Ferrer, J.M. Pena-Barragan, M. Jurado-Expósito, M.S. de la Orden, and M. González-Audicana, 2009. Object- and pixel-based analysis for mapping crops and their agro-environmental associated measures using Quick Bird imagery, Computers and Electronics in Agriculture, 68:207-215.
8. Chapelle, O., V. Vapnik, O. Bousquet, and S. Mukherjee, 2002. Choosing multiple parameters for support vector machines, Machine Learning, 46(1):131-159.
9. Christian, B., and N.S.R. Krishnayya, 2009. Classification of tropical trees growing in a sanctuary using Hyperion (EO-1) and SAM algorithm, Current Science, 96:1601-1607.
10. Clark, M.L., D.A. Roberts, and D.B. Clark, 2005. Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales, Remote Sensing of Environment, 96:375-398.
11. Cohen, J., 1960. A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20:37-46.
12. Duveiller, G., and P. Defourny, 2010. A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing, Remote Sensing of Environment, 114:2637-2650.
13. Foody, G.M., 2002. Status of land cover classification accuracy assessment, Remote Sensing of Environment, 80:185-201.
14. th International Symposium Remote Sensing of Urban Areas, 14-16 March, Tempe, Arizona, unpaginated CD-ROM.
15. Guo, L.J., and J.M. Moore, 1991. Post-classification processing for thematic mapping based on remotely sensed image data, Proceedings of the International Geoscience and Remote Sensing Symposium, pp. 2203-2206.
16. nd EARSEL Symposium Geoinformation for European-wide Integration, Prague, unpaginated CD-ROM.
17. Huang, C., L.S. Davis, and J.R.G. Townshend, 2002. An assessment of support vector machines for land cover classification, International Journal of Remote Sensing, 23(4):725-749.
18. Johansen, K., S. Phinn, C. Witte, S. Philip, and L. Newton, 2009. Mapping banana plantations from object-oriented classification of SPOT-5 imagery, Photogrammetric Engineering & Remote Sensing, 75(9):1069-1081.
19. Johansen, K., L.A. Arroyo, S. Phinn, and C. Witte, 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery, Photogrammetric Engineering & Remote Sensing, 76(2):123-136.
20. Kim, M., M. Madden, and T.A. Warner, 2009. Forest type mapping using object-specific texture measures from multispectral Ikonos Imagery: Segmentation quality and image classification issues, Photogrammetric Engineering & Remote Sensing, 75(7): 819-829.
21. Kim, S-R., W.-K. Lee, D.-A. Kwak, G.S. Biging, P. Gong, J.H. Lee, and H.-K. Cho, 2011. Forest cover classification by optimal segmentation of high resolution satellite imagery, Sensors, 11: 1943-1958.
22. th International Cartographic Conference of the ICA, Ottawa, Canada, unpaginated CD-ROM.
23. Li, D., G. Zhang, Z. Wu, and L. Yi, 2010. An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation, IEEE Transactions on Image Processing, 19(10):2781-2787.
24. th International Symposium on Physical Measurements and Signatures in Remote Sensing (ISPMSRS), 17-19 October, Beijing, unpaginated CD-ROM.
25. Melgani, F., and L. Bruzzone, 2004.Classification of hyperspectral remote sensing images with support vector machines, IEEE Transactions on Geoscience and Remote Sensing, 42:1778-1790.
26. Mitra, P., B.U. Shankar, and S.K. Pal, 2004. Segmentation of multi-spectral remote sensing images using active support vector machines, Pattern Recognition Letters, 25:1067-1074.
27. Ozdogan, M., and C.E. Woodcock, 2006. Resolution dependent errors in remote sensing of cultivated areas, Remote Sensing of Environment, 103:203-217.
28. Paola, J.D., and A. Schowengert, 1995. A detailed comparison of back propagation neural network and maximum-likelihood classifiers for urban land use classification, IEEE Transactions on Geoscience and Remote Sensing, 33(4):981-995.
29. Pax-Lenney, M., and C.E. Woodcock, 1997. The effect of spatial resolution on the ability to monitor the status of agricultural lands, Remote Sensing of Environment, 61:210-220.
30. Peña-Barragán, J.M., M.K. Ngugi, R.E. Plant, and J. Six, 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology, Remote Sensing of Environment, 115:1301-1316.
31. th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Paris, France, pp. 43-50.
32. Qian, Y., K. Zhang, and F. Qiu, 2005. Spatial contextual noise removal for post-classification smoothing of GIS images, Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, New Mexico, pp. 524-528.
33. Sertel, E., S.H.S. Kutoglu, and S. Kaya, 2007. Geometric correction accuracy of different satellite sensor images: Application of figure condition, International Journal of Remote Sensing, 28:4685-4692.
34. Sohn, Y., and N.S. Rebello, 2002. Supervised and unsupervised spectral angle classifiers, Photogrammetric Engineering & Remote Sensing, 68(12):1271-1280.
35. Story, M., and R.G. Congalton, 1986. Accuracy assessment: A user's perspective, Photogrammetric Engineering & Remote Sensing, 52(3):397-399.
36. Su, L., M.J. Chopping, A. Rango, J.V. Martonchik, and D.P.C. Peters, 2007. Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery, Remote Sensing of Environment, 107:299-311.
37. Tansey, K., I. Chambers, A. Anstee, A. Denniss, and A. Lamb, 2009. Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas, Applied Geography, 29:145-157.
38. TUIK, 2012. Turkish Statistical Institute Website, Crop Production Statistics Database, query for 2009 statistics, URL: http://tuikapp.tuik.gov.tr/bitkiselapp/bitkisel_ing.zul (last date accessed: 15 August 2013).
39. USGS website, 2013. Landsat 5 History. URL: http://landsat.usgs.gov/about_landsat5.php (last date accessed: 15 August 2013).
40. van der Sande, C.J., S.M. de Jong, and A.P.J. de Roo, 2003. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment, International Journal of Applied Earth Observation and Geoinformation, 4:217-229.
41. Van Deusen, P.C., 1996. Unbiased estimates of class proportions from thematic maps, Photogrammetric Engineering &Remote Sensing, 62(4):409-412
42. Vyas, D., N.S.R. Krishnayya, K.R. Manjunath, S.S. Ray, and S. Panigrahy, 2011. Evaluation of classifiers for processing Hyperion (EO-1) data of tropical vegetation, International Journal of Applied Earth Observation and Geoinformation, 13:228-235.
43. Zhang, Y., 2001. Texture-integrated classification of urban treed areas in high-resolution color-infrared imagery, Photogrammetric Engineering & Remote Sensing, 67(12):1359-1365.
44. Zhang, Y., 2004. Understanding image fusion, Photogrammetric Engineering & Remote Sensing, 70(6):657-661.
45. Zhou, W., and A. Troy, 2008. An object-oriented approach for analyzing and characterizing urban landscape at the parcel level, International Journal of Remote Sensing, 29(11), 3119-3135.