A comparison of object-based and contextual pixel-based classifications using high and medium spatial resolution images

Remote Sensing Letters

Volumn 4, Issue 10, 2013, Pages 998-1007

  Cai, Shanshan ^a   Liu, Desheng ^a  

^a OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85007884501     PISSN: 2150704X     EISSN: 21507058     Source Type: Journal    
DOI: 10.1080/2150704X.2013.828180     Document Type: Article

References (27)

1. BAATZ, M. and SCHÄPE, A., 2000, Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsverarbeitung XII. Beiträge zum AGIT-Symposium Salzburg 2000, Karlsruhe (Salzburg: Herbert Wichmann Verlag), pp. 12–23.
2. BENZ, U.C., HOFMANN, P., WILLHAUCK, G., LINGENFELDER, I. and HEYNEN, M., 2004, Multiresolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing, 58, pp. 239–258.
3. BESAG, J., 1986, On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society Series B-Methodological, 48, pp. 259–302.
4. BLASCHKE, T., 2010, Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, pp. 2–16.
5. BOUCHER, A., SETO, K.C. and JOURNEL, A.G., 2006, A novel method for mapping land cover changes: incorporating time and space with geostatistics. IEEE Transactions on Geoscience and Remote Sensing, 44, pp. 3427–3435.
6. CHANG, C.-C. and LIN, C.-J., 2011, LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, pp. 1–27.
7. CHUBEY, M.S., FRANKLIN, S.E. and WULDER, M.A., 2006, Object-based analysis of Ikonos- 2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering and Remote Sensing, 72, pp. 383–394.
8. CLEVE, C., KELLY, M., KEARNS, F.R. and MORLTZ, M., 2008, Classification of the wildland-urban interface: a comparison of pixel- and object-based classifications using high-resolution aerial photography. Computers Environment and Urban Systems, 32, pp. 317–326.
9. GAO, Y., MAS, J.F., MAATHUIS, B.H.P., ZHANG, X.M. and VAN DIJK, P.M., 2006, Comparison of pixel-based and object-oriented image classification approaches – a case study in a coal fire area, Wuda, Inner Mongolia, China. International Journal of Remote Sensing, 27, pp. 4039–4055.
10. GONG, P. and HOWARTH, P.J., 1989, Performance analyses of probabilistic relaxation methods for land-cover classification. Remote Sensing of Environment, 30, pp. 33–42.
11. HAMMERSLEY, J. and CLIFFORD, P., 1971, Markov fields on finite graphs and lattices. Unpublished manuscript, Oxford University.
12. 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, pp. 725–749.
13. LALIBERTE, A.S., RANGO, A., HAVSTAD, K.M., PARIS, J.F., BECK, R.F., MCNEELY, R. and GONZALEZ, A.L., 2004, Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment, 93, pp. 198–210.
14. LANDGREBE, D.A. (Ed.), 2003, Signal Theory Methods in Multispectral Remote Sensing (Hoboken, NJ: Wiley).
15. LI, S.Z. (Ed.), 2009, Markov Random Field Modeling in Image Analysis (London: Springer).
16. LIU, D. and CAI, S., 2012, A spatial-temporal modeling approach to reconstructing land-cover change trajectories from multi-temporal satellite imagery. Annals of the Association of American Geographers, 102, pp. 1329–1347.
17. LIU, D., KELLY, M. and GONG, P., 2006, A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sensing of Environment, 101, pp. 167–180.
18. LIU, D. and XIA, F., 2010, Assessing object-based classification: advantages and limitations. Remote Sensing Letters, 1, pp. 187–194.
19. LU, D. and WENG, Q., 2007, A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28, pp. 823–870.
20. LU, D.S., HETRICK, S. and MORAN, E., 2011, Impervious surface mapping with Quickbird imagery. International Journal of Remote Sensing, 32, pp. 2519–2533.
21. MELGANI, F. and SERPICO, S.B., 2003, A Markov random field approach to spatio-temporal contextual image classification. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 2478–2487.
22. NEWMAN, M.E., MCLAREN, K.P. and WILSON, B.S., 2011, Comparing the effects of classification techniques on landscape-level assessments: pixel-based versus objectbased classification. International Journal of Remote Sensing, 32, pp. 4055–4073.
23. PERSELLO, C. and BRUZZONE, L., 2010, A novel protocol for accuracy assessment in classification of very high resolution images. IEEE Transactions on Geoscience and Remote Sensing, 48, pp. 1232–1244.
24. PLATT, R.V. and RAPOZA, L., 2008, An evaluation of an object-oriented paradigm for land use/land cover classification. Professional Geographer, 60, pp. 87–100.
25. PONTIUS, R.G. and MILLONES, M., 2011, Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32, pp. 4407–4429.
26. ROBERTSON, L.D. and KING, D.J., 2011, Comparison of pixel- and object-based classification in land cover change mapping. International Journal of Remote Sensing, 32, pp. 1505–1529.
27. YU, Q., GONG, P., CLINTON, N., BIGING, G., KELLY, M. and SCHIROKAUER, D., 2006, Object based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogrammetric Engineering and Remote Sensing, 72, pp. 799–811.