Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 102, Issue , 2015, Pages 14-27

  Ma, Lei ^a   Cheng, Liang ^a   Li, Manchun ^a,b   Liu, Yongxue ^a,b   Ma, Xiaoxue ^a  

^a NANJING UNIVERSITY   (China)

^b NANJING NORMAL UNIVERSITY   (China)

Author keywords

GEOBIA; OBIA; Scale; Training set size; UAV; Very high resolution (VHR)

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE ANALYSIS; PIXELS; REMOTE SENSING; UNMANNED VEHICLES;

GEOBIA; OBIA; SCALE; TRAINING SETS; VERY HIGH RESOLUTION;

UNMANNED AERIAL VEHICLES (UAV);

ACCURACY ASSESSMENT; AUTOMATION; CORRELATION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RESOLUTION; NATURAL RESOURCE; OPTIMIZATION; REMOTE SENSING; SENSOR; UNMANNED VEHICLE;

EID: 84922357427     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.12.026     Document Type: Article

References (63)

1. Addink E.A., Van Coillie F., De Jong S.M. Introduction to the GEOBIA 2010 special issue: from pixels to geographic objects in remote sensing image analysis. Int. J. Appl. Earth Obs. Geoinf. 2012, 15:1-6.
2. Arvor D., Durieux L., Andrés S., Laporte M.A. Advances in Geographic Object-Based Image Analysis with ontologies: a review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogram. Remote Sens. 2013, 82:125-137.
3. 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. Photogram. Remote Sens. 2004, 58(3):239-258.
4. Blaschke T. Object based image analysis for remote sensing. ISPRS J. Photogram. Remote Sens. 2010, 65(1):2-16.
5. Blaschke T., Hay G.J., Kelly M., Lang S., Hofmann P., Addink E., et al. Geographic Object-Based Image Analysis-Towards a new paradigm. ISPRS J. Photogram. Remote Sens. 2014, 87:180-191.
6. Breiman L. Random forests. Machine Learning 2001, Springer, Netherlands, 5-32. R.E. Schapire (Ed.).
7. Burns E., Lemons S., Ruml W., Zhou R. Best-First heuristic search for multicore machines. J. Artific. Intell. Res. 2010, 39:689-743.
8. Chan J.C., Paelinckx D.E. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens. Environ. 2008, 112(6):2999-3011.
9. Cheng L., Gong J., Yang X., Fan C., Han P. Robust affine invariant feature extraction for image matching. IEEE Geosci. Remote Sens. Lett. 2008, 5(2):246-251.
10. Cheng L., Li M., Liu Y., Cai W., Chen Y., Yang Kang Remote sensing image matching by integrating affine invariant feature extraction and RANSAC. Comput. Electr. Eng. 2012, 38(4):1023-1032.
11. Cheng L., Tong L., Liu Y., Li M., Wang J. Automatic registration of coastal remotely sensed imagery by affine invariant feature matching with shoreline constraint. Mar. Geodesy 2014, 37(1):32-46.
12. Colomina I., Molina P. Unmanned aerial systems for photogrammetry and remote sensing: a review. ISPRS J. Photogram. Remote Sens. 2014, 92:79-97.
13. Congalton R.G., Green K. Assessing the accuracy of remotely sensed data: principles and practices 2009, CRC/Taylor and Francis Group, LLC, Boca Raton, London, New York.
14. Costa H., Carrao H., Bacso F., Caetano M. Combining per-pixel and object-based classifications for mapping land cover over large areas. Int. J. Remote Sens. 2014, 35(2):738-753.
15. Dong L., Shan J. A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS J. Photogram. Remote Sens. 2013, 84:85-99.
16. Drağuţ L., Tiede D., Levick S. ESP: a tool to estimate scale parameters for multiresolution image segmentation of remotely sensed data. Int. J. Geograph. Inform. Sci. 2010, 24(6):859-871.
17. Drağuţ L., Csillik O., Eisank C., Tiede D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogram. Remote Sens. 2014, 88:119-127.
18. Dunford R., Michel K., Gagnage M., Piegay H., Tremelo M.L. Potential and constraints of unmanned aerial vehicle technology for the characterization of mediterranean riparian forest. Int. J. Remote Sens. 2009, 30(19):4915-4935.
19. Duro D.C., Franklin S.E., Dube M.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(15):259-272.
20. Foody G.M., Mathur A., Sanchez-Hernandez C., Boyd D.S. Training set size requirements for the classification of a specific class. Remote Sens. Environ. 2006, 104(1):1-14.
21. Freire S., Santos T., Navarro A., Soares F., Silva J.D., Afonso N., et al. Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery. ISPRS J. Photogram. Remote Sens. 2014, 90:1-9.
22. Gomez-Candon D., De Castro A.I., Lopez-Granados F. Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat. Precision Agric. 2014, 15(1):44-56.
23. Guyon I., Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 2003, 3:1157-1182.
24. Hall M.A., Holmes B. Benchmarking attribute selection techniques for discrete class data mining. IEEE Trans. Knowledge Data Eng. 2003, 15(6):1-16.
25. Hall M., Frank E., Holmes G., Pfahringer B., Reutemann P., Witten I.H. The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 2009, 11(1):10-18.
26. Hay G.J., Castilla G. Geographic Object-Based Image Analysis (GEOBIA): a new name for a new discipline. Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications 2008, 75-89. Springer, Berlin, Heidelberg. T. Blaschke, S. Lang, G.J. Hay (Eds.).
27. Huang C.J., Yang D.X., Chuang Y.T. Application of wrapper approach and composite classifier to the stock trend prediction. Expert Syst. Appl. 2008, 34(4):2870-2878.
28. Hussain M., Chen D.M., Cheng A., Wei H., Stanley D. Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogram. Remote Sens. 2013, 80:91-106.
29. Jaakkola A., Hyyppa J., Kukko A., Yu X., Kaartinen H., Lehtomäki M., et al. A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS J. Photogram. Remote Sens. 2010, 65(6):514-522.
30. Johnson B., Xie Z. Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogram. Remote Sens. 2011, 66(4):473-483.
31. Johnson B., Xie Z. Classifying a high resolution image of an urban area using super-object information. ISPRS J. Photogram. Remote Sens. 2013, 83:40-49.
32. Kim M., Warner T.A., Madden M., Atkinson D.S. Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects. Int. J. Remote Sens. 2011, 32(10):2825-2850.
33. Kim J., Lee S., Ahn H., Seo D., Park S., Choi C. Feasibility of employing a smartphone as the payload in a photogrammetric UAV system. ISPRS J. Photogram. Remote Sens. 2013, 79:1-18.
34. Kurtz C., Stumpf A.E., Malet J., Gancarski P., Puissant A., Passat N. Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS J. Photogram. Remote Sens. 2014, 87:122-136.
35. Laliberte A.S., Rango A. Texture and scale in Object-based analysis of subdecimeter resolution Unmanned Aerial Vehicle (UAV) imagery. IEEE Trans. Geosci. Remote Sens. 2009, 47(3):761-770.
36. Laliberte A.S., Browning D.M., Rango A. A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery. Int. J. Appl. Earth Obs. Geoinf. 2012, 15:70-78.
37. Liu Y.X., Li M.C., Mao L., Xu F., Huang S. Review of remotely sensed imagery classification patterns based on object-oriented image analysis. Chin. Geograph. Sci. 2006, 16(3):282-288.
38. Lucieer A., Turner D., King D.H., Robinson S.A. Using an Unmanned Aerial Vehicle (UAV) to capture micro-topography of Antarctic moss beds. Int. J. Appl. Earth Obs. Geoinf. 2014, 27:53-62.
39. Ma, L., Li, M.C., Tong, L.H., Wang, Y.F., Cheng, L., 2013a. Using unmanned aerial vehicle for remote sensing application. In: 2013 21st International Conference on Geoinformatics, Kaifeng, China, pp. 1-5.
40. Ma, L., Wang, Y.F., Li, M.C., Tong, L.H., Cheng, L., 2013b. Using High-Resolution Imagery Acquired with an Autonomous Unmanned Aerial Vehicle for Urban Construction and Planning. In: International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013), Najing, China.
41. Ma L., Cheng L., Han W.Q., Zhong L.S., Li M.C. Cultivated land information extraction from high-resolution unmanned aerial vehicle imagery data. J. Appl. Remote Sens. 2014, 8(1):83673.
42. Myint S.W., Gober P., Brazel A., Grossman-Clarke S., Weng Q.H. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 2011, 115(5):1145-1161.
43. Novack T., Esch T., Kux H., Stilla U. Machine learning comparison between WorldView-2 and QuickBird-2-Simulated imagery regarding Object-Based urban land cover classification. Remote Sens. 2011, 3(10):2263-2282.
44. Pal M., Mather P.M. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 2003, 86(4):554-565.
45. Pal M., Foody G.M. Feature selection for classification of Hyperspectral data by SVM. IEEE Trans. Geosci. Remote Sens. 2010, 48(5):2297-2307.
46. Powers R.P., Hay G.J., Chen G. How wetland type and area differ through scale: a GEOBIA case study in Alberta's Boreal Plains. Remote Sens. Environ. 2012, 117(15):135-145.
47. Press W.H., Flannery B.P., Teukolsky S., Vetterling W.T. Numerical recipes 1988, Cambridge University Press, Cambridge, UK.
48. Puissant A., Rougier S., Stumpf A.E. Object-oriented mapping of urban trees using Random Forest classifiers. Int. J. Appl. Earth Obs. Geoinf. 2014, 26:235-245.
49. Quinlan J.R. Improved use of continuous attributes in C4.5. J. Artific. Intell. Res. 1996, 4:77-90.
50. Radoux J., Bogaert P. Accounting for the area of polygon sampling units for the prediction of primary accuracy assessment indices. Remote Sens. Environ. 2014, 142:9-19.
51. Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J.P. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J. Photogram. Remote Sens. 2012, 67:93-104.
52. Stehman S.V., Wickham J.D. Pixels, blocks of pixels, and polygons: choosing a spatial unit for thematic accuracy assessment. Remote Sens. Environ. 2011, 115(12):3044-3055.
53. Stefanski J., Mack B., Waske B.O.R. Optimization of object-based image analysis with random forests for land cover mapping. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 2013, 6(6):2492-2504.
54. Sugiura R., Noguchi N., Ishii K. Remote-sensing technology for vegetation monitoring using an unmanned helicopter. Biosyst. Eng. 2005, 90(4):369-379.
55. Trimble Documentation eCognition® Developer 8.7 Reference Book, München, Germany, 22 September 2011.
56. Van Niel T.G., McVicar T.R., Datt B. On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification. Remote Sens. Environ. 2005, 98(4):468-480.
57. Verikas A., Gelzinis A., Bacauskiene M. Mining data with random forests: a survey and results of new tests. Pattern Recogn. 2011, 44(2):330-349.
58. Whiteside T.G., Maier S.W., Boggs G.S. Area-based and location-based validation of classified image objects. Int. J. Appl. Earth Obs. Geoinf. 2014, 28:117-130.
59. Witharana C., Civco D.L. Optimizing multi-resolution segmentation scale using empirical methods: exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2). ISPRS J. Photogram. Remote Sens. 2014, 87:108-121.
60. Zhang C.H., Kovacs J.M. The application of small unmanned aerial systems for precision agriculture: a review. Precision Agric. 2012, 13(6):693-712.
61. Zhang X.L., Xiao P.F., Song X.Q., She J.F. Boundary-constrained multi-scale segmentation method for remote sensing images. ISPRS J. Photogram. Remote Sens. 2013, 78:15-25.
62. Zhen Z., Quackenbush L.J., Stehman S.V., Zhang L. Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification. Int. J. Remote Sens. 2013, 34(19):6914-6930.
63. Zhou G., Ambrosia V., Gasiewski A.J., Bland G. Foreword to the special issue on Unmanned Airborne Vehicle (UAV) sensing systems for earth observations. IEEE Trans. Geosci. Remote Sens. 2009, 47(3):687-689.