A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery

GIScience and Remote Sensing

Volumn 54, Issue 4, 2017, Pages 515-533

  Grybas, Heather ^a   Melendy, Lindsay ^a   Congalton, Russell G ^a  

^a UNIVERSITY OF NEW HAMPSHIRE   (United States)

Author keywords

OBIA; remote sensing; segmentation evaluation; unsupervised segmentation

Indexed keywords

IKONOS; IMAGE PROCESSING; IMAGE RESOLUTION; IMAGERY; LANDSAT THEMATIC MAPPER; OPTIMIZATION; REMOTE SENSING;

EID: 85011844325     PISSN: 15481603     EISSN: None     Source Type: Journal    
DOI: 10.1080/15481603.2017.1287238     Document Type: Article

References (47)

1. Baatz, M., and A., Schape. 2000. “Multiresolution Segmentation:An Optimization Approach for High Quality Multi-Scale Image Segmentation.” In Angewandte Geographische Informationsverarbeitung XII, edited by J., Strobl, T., Blaschke, and G., Griesebner, 12–23. Heidelberg:Wichmann-Verlag.
2. Belgiu, M., and L., Drăguţ. 2014. “Comparing Supervised and Unsupervised Multiresolution Segmentation Approaches for Extracting Buildings from Very High Resolution Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 96:67–75. doi:10.1016/j.isprsjprs.2014.07.002.
3. Blaschke, T., 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1):2–16. doi:10.1016/j.isprsjprs.2009.06.004.
4. Blaschke, T., C., Burnett, and A., Pekkarinen. 2004. “Image Segmentation Methods for Object-Based Analysis and Classification.” In Remote Sensing Image Analysis:Including the Spatial Domain, edited by S. M., De Jong and F. D., Van Der Meer, 211–236. Dordrecht, Netherlands:Kluver Academic Publishers. doi:10.1007/978-1-4020-2560-0.
5. Campbell, M., R. G., Congalton, J., Hartter, and M., Ducey. 2015. “Optimal Land Cover Mapping and Change Analysis in Northeastern Oregon Using Landsat Imagery.” Photogrammetric Engineering & Remote Sensing 81 (1):37–47. doi:10.14358/PERS.81.1.37.
6. Cánovas-García, F., and F., Alonso-Sarría. 2015. “A Local Approach to Optimize the Scale Parameter in Multiresolution Segmentation for Multispectral Imagery.” Geocarto International 30 (8):937–961. doi:10.1080/10106049.2015.1004131.
7. Chabrier, S., B., Emile, C., Rosenberger, and H., Laurent. 2006. “Unsupervised Performance Evaluation of Image Segmentation.” EURASIP Journal on Advances in Signal Processing 2006:1–12. doi:10.1155/ASP/2006/96306.
8. Cheng, J., Y., Bo, Y., Zhu, and X., Ji. 2014. “A Novel Method for Assessing the Segmentation Quality of High-Spatial Resolution Remote-Sensing Images.” International Journal of Remote Sensing 35 (10):3816–3839. doi:10.1080/01431161.2014.919678.
9. Clinton, N., A., Holt, J., Scarborough, L., Yan, and P., Gong. 2010. “Accuracy Assessment Measures for Object-Based Image Segmentation Goodness.” Photogrammetric Engineering Remote Sensing 76 (3):289–299. doi:10.14358/PERS.76.3.289.
10. Conchedda, G., L., Durieux, and P., Mayaux. 2008. “An Object-Based Method for Mapping and Change Analysis in Mangrove Ecosystems.” ISPRS Journal of Photogrammetry and Remote Sensing 63 (5):578–589. doi:10.1016/j.isprsjprs.2008.04.002.
11. Dial, G., H., Bowen, F., Gerlach, J., Grodecki, and R., Oleszczuk. 2003. “IKONOS Satellite, Imagery, and Products.” Remote Sensing of Environment 88 (1–2):23–36. doi:10.1016/j.rse.2003.08.014.
12. Drăguţ, L., O., Csillik, C., Eisank, and D., Tiede. 2014. “Automated Parameterisation for Multi-Scale Image Segmentation on Multiple Layers.” ISPRS Journal of Photogrammetry and Remote Sensing 88:119–127. doi:10.1016/j.isprsjprs.2013.11.018.
13. Drǎguţ, L., D., Tiede, and S. R., Levick. 2010. “ESP:A Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data.” International Journal of Geographical Information Science 24 (6):859–871. doi:10.1080/13658810903174803.
14. Duro, D. C., S. E., Franklin, and M. G., Dubé. 2012. “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 Sensing of Environment 118:259–272. doi:10.1016/j.rse.2011.11.020.
15. Espindola, G. M., G., Camara, I. A., Reis, L. S., Bins, and A. M., Monteiro. 2006. “Parameter Selection for Region‐Growing Image Segmentation Algorithms Using Spatial Autocorrelation.” International Journal of Remote Sensing 27 (14):3035–3040. doi:10.1080/01431160600617194.
16. Ferguson, R. L., and K., Korfmacher. 1997. “Remote Sensing and GIS Analysis of Seagrass Meadows in North Carolina, USA.” Aquatic Botany 58 (3–4):241–258. doi:10.1016/S0304-3770(97)00038-7.
17. Flanders, D., M., Hall-Beyer, and J., Pereverzoff. 2003. “Preliminary Evaluation of Ecognition Object-Based Software for Cut Block Delineation and Feature Extraction.” Canadian Journal of Remote Sensing 29 (4):441–452. doi:10.5589/m03-006.
18. Fotheringham, A. S., C., Brunsdon, and M. E., Charlton. 2000. Quantitative Geography:Perspectives on Spatial Data Analysis. London:Sage. doi:10.1016/S0143-6228(97)90005-9.
19. Gao, Y., J., Francois Mas, N., Kerle, and J. A. N., Pacheco. 2011. “Optimal Region Growing Segmentation and Its Effect on Classification Accuracy.” International Journal of Remote Sensing 32 (13):3747–3763. doi:10.1080/01431161003777189.
20. Guo, Z., and S., Du. 2017. “Mining Parameter Information for Building Extraction and Change Detection with Very High-Resolution Imagery and GIS Data.” Giscience & Remote Sensing 54 (1):38–63. Taylor & Francis. doi:10.1080/15481603.2016.1250328.
21. Jensen, J. R., 2016. Introductory Digital Image Processing:A Remote Sensing Perspective. 4th ed. Upper Saddle River, NJ:Pearson Prentice Hall.
22. 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. doi:10.14358/PERS.76.2.123.
23. Johnson, B., M., Bragais, I., Endo, D. M., Macandog, and P., Macandog. 2015. “Image Segmentation Parameter Optimization considering Within- and Between-Segment Heterogeneity at Multiple Scale Levels:Test Case for Mapping Residential Areas Using Landsat Imagery.” ISPRS International Journal of Geo-Information 4 (4):2292–2305. doi:10.3390/ijgi4042292.
24. Johnson, B., and Z., Xie. 2011. “Unsupervised Image Segmentation Evaluation and Refinement Using a Multi-Scale Approach.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (4):473–483. doi:10.1016/j.isprsjprs.2011.02.006.
25. Johnson, B., and Z., Xie. 2013. “Classifying a High Resolution Image of an Urban Area Using Super-Object Information.” ISPRS Journal of Photogrammetry and Remote Sensing 83:40–49. doi:10.1016/j.isprsjprs.2013.05.008.
26. Kim, M., M., Madden, and T., Warner. 2008. “Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery.” In Object-Based Image Analysis - Spatial Concepts for Knowledge-Driven Remote Sensing Applications, edited by T., Blaschke, S., Lang, and G. J., Hay, 291–307. Heidelberg, Germany:Springer. doi:10.1007/978-3-540-77058-9_16.
27. 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. doi:10.14358/PERS.75.7.819.
28. Kim, M., T. A., Warner, M., Madden, and D. S., Atkinson. 2011. “Multi-Scale GEOBIA with Very High Spatial Resolution Digital Aerial Imagery:Scale, Texture and Image Objects.” International Journal of Remote Sensing 32 (10):2825–2850. doi:10.1080/01431161003745608.
29. Lathrop, R. G., P., Montesano, and S., Haag. 2006. “A Multi-Scale Segmentation Approach to Mapping Seagrass Habitats Using Airborne Digital Camera Imagery.” Photogrammetric Engineering & Remote Sensing 72 (6):665–675. doi:10.14358/PERS.72.6.665.
30. Lawrence, R. L., and A., Wright. 2001. “Rule-Based Classification Systems Using Classification and Regression Tree (CART) Analysis.” Photogrammetric Engineering and Remote Sensing 67 (10):1137–1142.
31. Liu, Y., L., Bian, Y., Meng, H., Wang, S., Zhang, Y., Yang, X., Shao, and B., Wang. 2012. “Discrepancy Measures for Selecting Optimal Combination of Parameter Values in Object-Based Image Analysis.” ISPRS Journal of Photogrammetry and Remote Sensing 68:144–156. doi:10.1016/j.isprsjprs.2012.01.007.
32. Möller, M., L., Lymburner, and M., Volk. 2007. “The Comparison Index:A Tool for Assessing the Accuracy of Image Segmentation.” International Journal of Applied Earth Observation and Geoinformation 9 (3):311–321. doi:10.1016/j.jag.2006.10.002.
33. Platt, R. V., and L., Rapoza. 2008. “An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification.” The Professional Geographer 60 (1):87–100. doi:10.1080/00330120701724152.
34. Räsänen, Aleksi, Antti Rusanen, Markku Kuitunen, and Anssi Lensu. 2013. “What Makes Segmentation Good? A Case Study in Boreal Forest Habitat Mapping.” International Journal of Remote Sensing 34 (23):8603–27. doi:10.1080/01431161.2013.845318
35. Tong, H., T., Maxwell, Y., Zhang, and V., Dey. 2012. “A Supervised and Fuzzy-Based Approach to Determine Optimal Multi-Resolution Image Segmentation Parameters.” Photogrammetric Engineering & Remote Sensing 78 (10):1029–1044. doi:10.14358/PERS.78.10.1029.
36. Trimble. 2014. Ecognition Developer 9.0 User Guide. Munich, Germany:TrimbleGermany GmbH.
37. Vogelmann, J. E., T., Sohl, and S. M., Howard. 1998. “Regional Characterization of Land Cover Using Multiple Sources of Data.” Photogrammetric Engineering & Remote Sensing 64 (1):45–57.
38. Whiteside, T. G., S. W., Maier, and G. S., Boggs. 2014. “Area-Based and Location-Based Validation of Classified Image Objects.” International Journal of Applied Earth Observation and Geoinformation 28 (May):117–130. doi:10.1016/j.jag.2013.11.009.
39. Witten, I. H., E., Frank, and M. A., Hall. 2011. Data Mining:Practical Machine Learning Tools and Techniques. 3rd ed. Burlington, MA:Morgan Kaufmann.
40. Woodcock, C. E., and A. H., Strahler. 1987. “The Factor of Scale in Remote Sensing.” Remote Sensing of Environment 21 (3):311–332. doi:10.1016/0034-4257(87)90015-0.
41. Xun, L., and L., Wang. 2015. “An Object-Based SVM Method Incorporating Optimal Segmentation Scale Estimation Using Bhattacharyya Distance for Mapping Salt Cedar (Tamarisk Spp.) with Quickbird Imagery.” Giscience & Remote Sensing 52 (3):257–273. Taylor & Francis. doi:10.1080/15481603.2015.1026049.
42. Yang, J., P., Li, and Y., He. 2014. “A Multi-Band Approach to Unsupervised Scale Parameter Selection for Multi-Scale Image Segmentation.” ISPRS Journal of Photogrammetry and Remote Sensing 94:13–24. doi:10.1016/j.isprsjprs.2014.04.008.
43. Yu, Q., P., Gong, N., Clinton, G., Biging, M., Kelly, and D., Schirokauer. 2006. “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery.” Photogrammetric Engineering & Remote Sensing 72 (7):799–811. doi:10.14358/PERS.72.7.799.
44. Zhang, H., J. E., Fritts, and S. A., Goldman. 2008. “Image Segmentation Evaluation:A Survey of Unsupervised Methods.” Computer Vision and Image Understanding 110:260–280. doi:10.1016/j.cviu.2007.08.003.
45. Zhang, L., K., Jia, X., Li, Q., Yuan, and X., Zhao. 2014. “Multi-Scale Segmentation Approach for Object-Based Land-Cover Classification Using High-Resolution Imagery.” Remote Sensing Letters 5 (1):73–82. doi:10.1080/2150704X.2013.875235.
46. Zhang, X., X., Feng, P., Xiao, G., He, and L., Zhu. 2015. “Segmentation Quality Evaluation Using Region-Based Precision and Recall Measures for Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 102:73–84. doi:10.1016/j.isprsjprs.2015.01.009.
47. Zhang, X., P., Xiao, and X., Feng. 2012. “An Unsupervised Evaluation Method for Remotely Sensed Imagery Segmentation.” IEEE Geoscience and Remote Sensing Letters 9 (2):156–160. doi:10.1109/LGRS.2011.2163056.