Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 106, Issue , 2015, Pages 28-41

  Ming, Dongping ^a   Li, Jonathan ^b   Wang, Junyi ^b   Zhang, Min ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

^b UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Average local variance; GEOBIA; Mean shift segmentation; Scale selection; Statistics

Indexed keywords

BANDWIDTH; IMAGE PROCESSING; IMAGE RECONSTRUCTION; INFORMATION ANALYSIS; INFORMATION RETRIEVAL; MERGING; REMOTE SENSING; STATISTICS;

GEOBIA; HIGH SPATIAL RESOLUTION; LOCAL VARIANCE; MEAN-SHIFT SEGMENTATION; MULTI-SCALE IMAGE SEGMENTATION; MULTISCALE SEGMENTATION; SCALE SELECTION; SEGMENTATION ALGORITHMS;

IMAGE SEGMENTATION;

GRAPHICAL METHOD; HISTOGRAM; IKONOS; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PANCHROMATIC IMAGE; PARAMETERIZATION; QUICKBIRD; SPATIAL ANALYSIS; VARIANCE ANALYSIS; VARIOGRAM;

AVIAN LEUKOSIS VIRUS;

EID: 84929448899     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2015.04.010     Document Type: Article

References (58)

1. Baatz, M., 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, Herbert Wichmann Verlag, pp. 12-23.
2. Baatz, M., Shäpe, A., 1999. Object-oriented and multi-scale image analysis in semantic networks. In: Proceeding of the 2nd International Symposium on Operationalization of Remote Sensing, August 16th-20th, Enschede. ITC, Enschede, The Netherlands.
3. Baatz M., Benz U.C., Dehghani S., Heynen M., Höltje A., Hofmann P., Lingenfelder I., Mimler M., Sohlbach M., Weber M., Willhauck G. ECognition User guide 2000, Definiens-Imaging, Munich, Germany.
4. Benz U.C., Hofmann P., Willhauck G., Lingenfelder I., Heynen M. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58:239-258.
5. Blaschke T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65(1):2-16.
6. Blaschke T., Burnett C., Pekkarinen A. Image segmentation methods for object-based analysis and classification. Remote Sensing and Digital Image Analysis. Including the Spatial Domain. Book Series: Remote Sensing and Digital Image Processing 2004, vol. 5:211-236. Kluwer Academic Publishers, Dordrecht, (Chapter 12).
7. Blaschke T., Lang S., Hay G. Object-based Image Analysis: Spatial Concepts for Knowledge-driven Remote Sensing Applications 2008, Springer-Verlag, Berlin.
8. Blaschke T., Hay G.J., Kelly M., Lang S., et al. Geographic object-based image analysis - towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87:180-191.
9. Cheng Y. Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 1995, 17(8):790-799.
10. Claudio R.J. Combining wavelets and watersheds for robust multiscale image segmentation. Image Vis. Comput. 2007, 25(1):24-33.
11. Comaniciu D. An algorithm for data-driven bandwidth selection. IEEE Trans. Pattern Anal. Mach. Intell. 2003, 25(2):281-288.
12. Comaniciu D., Meer P. Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(5):603-619.
13. Comaniciu, D., Ramesh, V., Meer, P., 2001. The variable bandwidth mean shift and data-driven scale selection. In: Proc. Eighth Int'l Conf. Computer Vision, vol. I, pp. 438-445.
14. Definiens AG, 2007. Definiens Developer 7 User Guide, Version 7.0.2.936, 23.
15. Drăgut L., et al. Optimization of scale and parametrization for terrain segmentation: an application to soil-landscape modeling. Comput. Geosci. 2009, 35:1875-1883.
16. Drăgut L., Tiede D., Levick S.R. ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inform. Sci. 2010, 24(6):859-871.
17. Dronova I., Gong P., Clinton N.E., Wang L., Fu W., Qi S., Liu Y. Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sens. Environ. 2012, 127:357-369.
18. Duro D.C., Franklin S.E., DubéM G. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens. Environ. 2012, 118:259-272.
19. Eid M.E., Khaled M.E., Onsi H.M. A proposed multi-scale approach with automatic scale selection for image change detection. Egypt. J. Remote Sens. Space Sci. 2010, 13:1-10.
20. Einbeck J. Bandwidth selection for mean-shift based unsupervised learning techniques: a unified approach via self-coverage. J. Pattern Recognit. Res. 2011, 2:175-192.
21. Espindola G., Camara G., Reis I., Bins L., Monteiro A. Parameter selection for region growing image segmentation algorithms using spatial autocorrelation. Int. J. Remote Sens. 2006, 27(14):3035-3040.
22. Fukunaga K., Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Trans. Inf. Theory 1975, 21:32-40.
23. Hay G.J., Castilla G. Geographic Object-Based Image Analysis (GEOBIA): a new name for a new discipline. Object-Based Image Anal. Lect. Notes Geoinform. Cartogr. 2008, 2008:75-89. 10.1007/978-3-540-77058-9_4.
24. Hay G.J., Castilla G., Wulder M.A., Ruiz J.R. An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Obs. Geoinf. 2005, 7(4):339-359.
25. He M., Zhang W., Wang W. Optimal segmentation scale model based on object-oriented analysis method. J. Geodesy Geodyn. 2009, 29(1):106-109.
26. Johnson B., Xie Z. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens. 2011, 66:473-483.
27. Karl J.W., Maurer B.A. Spatial dependency of predictions from image segmentation: a variogram-based method to determine appropriate scales for producing land-management information. Ecol. Inform. 2010, 5:194-202.
28. Kim M., Madden M., Warner T. Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery. Object-based Image Analysis-Spatial Concepts for Knowledge Driven Remote Sensing Applications 2008, 291-307. Springer, Berlin. T. Blaschke, S. Lang, G.J. Hay (Eds.).
29. Li P., Xiao X. Multispectral image segmentation by a multichannel watershed-based approach. Int. J. Remote Sens. 2007, 28:4429-4452.
30. Li X.R., Wu F.C., Hu Z.Y. Convergence of a mean shift algorithm. J. Software 2005, 16(3):365-374.
31. Li X.R., Hu Z.Y., Wu F.C. A note on the convergence of the mean shift. Pattern Recogn. 2007, 40:1756-1762.
32. Ma, Y., 2014. Analysis of Object-Oriented Scale Selection and its Scale Effect Based on e-Cognition. Dissertation Bachelor's Degree in Geoinformatics. China University of Geosciences, Beijing.
33. Ming, D., Luo, J., Li, L., Song, Z., 2010. Modified local variance based method for selecting the optimal spatial resolution of remote sensing image. In: Proceeding of The 18th International Conference on Geoinformatics, IEEE. doi: . doi:10.1109/GEOINFORMATICS.2010.5567566.
34. Ming D., Yang J., Li L., Song Z. Modified ALV for selecting the optimal spatial resolution and its scale effect on image classification accuracy. Math. Computer Model. 2011, 54(3-4):1061-1068.
35. Ming D., Ci T., Cai H., Li L., Qiao C. Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geosci. Remote Sens. Lett. 2012, 9(5):813-817.
36. Myint S.W., Gober P., Brazel A., Grossman-Clarke S., Weng Q. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115(5):1145-1161.
37. Anders N.S., Seijmonsbergen A.C., Bouten W. Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 2011, 115(12):2976-2985.
38. Openshaw S. The modifiable areal unit problem. Concepts and Techniques in Modern Geography 1984, vol. 38. GeoBooks, Norwich, England.
39. Park J.H., Lee G.S., Park S.Y. Color image segmentation using adaptive mean shift and statistical model-based methods. Comput. Math. Appl. 2009, 57:970-980.
40. Peña-Barragán J.M., Ngugi M.K., Plant R.E., Six J. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 2011, 115:1301-1316.
41. Polat E., Ozden M. A nonparametric adaptive tracking algorithm based on multiple feature distributions. IEEE Trans. Multimedia 2006, 8(6):1156-1163.
42. Rao S., Martins A.D.M., Príncipe J.C. Mean shift: an information theoretic perspective. Pattern Recogn. Lett. 2009, 30:222-230.
43. Scheunders, P., Sijbers, J., 2002. Multiscale watershed segmentation of multivalued images. In: 16th Proceedings of International Conference on Pattern Recognition (ICPR'02), vol. 3, p. 30855.
44. Sun J., Xu Z. Scale selection for anisotropic diffusion filter by Markov random field model. Pattern Recogn. 2010, 43:2630-2645.
45. Tan Q., Liu Z., Shen W. An algorithm for object_oriented multi_scale remote sensing image segmentation. J. Beijing Jiaotong Univ. 2007, 31(4):11-115.
46. Tian J., Chen D.M. Optimization in multi-scale segmentation of high-resolution satellite images for artificial feature recognition. Int. J. Remote Sens. 2007, 28(20):4625-4644.
47. Vieira M.A., Formaggio A.R., Rennó C.D., Atzberger C., Aguiar D.A., Mello M.P. Object based image analysis and data mining applied to a remotely sensed landsat time-series to map sugarcane over large areas. Remote Sens. Environ. 2012, 123:553-562.
48. Vincent L., Soille P. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13(6):583-598.
49. Vojir T., Noskova J., Matas J. Robust scale-adaptive mean-shift for tracking. Pattern Recognit. Lett. 2014, 10.1016/j.patrec.2014.03.025.
50. Wang G., Gertner G., Xiao X., et al. Appropriate plot size and spatial resolution for mapping multiple vegetation types. Photogramm. Eng. Remote Sens. 2001, 67(5):575-584.
51. Woodcock C.E., Strahler A.H. The factor of scale in remote sensing. Remote Sens. Environ. 1987, 21:311-332.
52. Wu K.-L., Yang M.-S. Mean shift-based clustering. Pattern Recogn. 2007, 40:3035-3052.
53. Yang G., Pu R., Zhang J., Zhao C., Feng H., Wang J. Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas. ISPRS J. Photogramm. Remote Sens. 2013, 77:79-93.
54. Zhang R. Spatial Variability Theory and its Applications 2005, Science Press, Beijing, China.
55. Zhang X., Cui Y., Li D., Liu X., Zhang F. An adaptive mean shift clustering algorithm based on locality-sensitive hashing. Optik 2012, 123:1891-1894.
56. Zhao M., Li F., Tang G. Optimal scale selection for DEM based slope segmentation in the loess plateau. Int. J. Geosci. 2012, 3:37-43. 10.4236/ijg.2012.31005.
57. Zheng L., Shi D., Zhang J. Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant. Pattern Recogn. Lett. 2010, 31:920-925.
58. Zhou H., Li X., Schaefer G., Celebi M.E., Miller P. Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Underst. 2013, 117:1004-1016.