1,131
Views
46
CrossRef citations to date
0
Altmetric
Articles

Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data

, , &
Pages 1876-1899 | Received 11 Mar 2013, Accepted 19 Dec 2013, Published online: 25 Feb 2014

References

  • Aguilar, M. A., M. M. Saldaña, and F. J. Aguilar. 2012. “GeoEYE-1 and WORldVIEW-2pan-Sharpened Imagery for Object-Based Classification in Urban Environments.” International Journal of Remote Sensing 34 (7): 2583–2606.
  • Almeida, C. M., I. M. Souza, C. D. Alves, C. M. Pinho, M. N. Pereira, and R. Q. Feitosa. 2007. “Multilevel Object-Oriented Classification of Quickbird Images for Urban Population Estimates.” In Proceedings of the 15th Annual ACM International Symposium on Advances in Geographic Information Systems, GIS ‘07, November 7–9, 2007, Seattle, Washington. 1–8. New York: ACM. doi: acm.org/10.1145/1341012.1341029.
  • Baatz, M., and A. Schape. 2000. “Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation.” In Angewandte Geographische Informationsverabeitung. XII. Beitragezum AGIT-Symp, edited by Salzburg, T. Strobl, T. Blaschke, and G. Griesebner, 12–23. Karlsruhe: Wichmann Verlag.
  • Ballo, S., M. Liu, L. Hou, and J. Chang. 2009. “Pollutants in Storm Water Runoff in Shanghai (China): Implications for Management of Urban Runoff Pollution.” Progress in Natural Science 19 (7): 873–880.
  • Bhaskaran, S., E. Nez, K. Jimenez, and S. K. Bhatia. 2012. “Rule-Based Classification of High-Resolution Imagery Over Urban Areas in New York City.” Geocarto International 1–19. doi:10.1080/01431161.2013.879350.
  • 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 (4): 650–665.
  • Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16.
  • Blaschke, T., and J. Strobl. 2001. “What’s Wrong with Pixels? Some Recent Developments Interfacing Remote Sensing and GIS.” Journal of Spatial Information and Decision Making 14 (6): 12–17.
  • Carleer, A., and E. Wolff. 2006. “Urban Land Cover Multi‐Level Region‐Based Classification of VHR Data by Selecting Relevant Features.” International Journal of Remote Sensing 27 (6): 1035–1051.
  • Chen, G., G. J. Hay, and B. St-Onge. 2012. “A GEOBIA Framework to Estimate Forest Parameters From Lidar Transects, Quickbird Imagery and Machine Learning: A Case Study in Quebec, Canada.” International Journal of Applied Earth Observation and Geoinformation 15 (0): 28–37.
  • Chen, Y., W. Su, J. Li, and Z. Sun. 2009. “Hierarchical Object Oriented Classification Using Very High Resolution Imagery and LIDAR Data Over Urban Areas.” Advances in Space Research 43 (7): 1101–1110.
  • Clinton, N., A. Holt, J. Scarborough, L. Yan, and P. Gong. 2010. “Accuracy Assessment Measures for Object-Based Image Segmentation Goodness.” Photogrammetric Engineering and Remote Sensing 76 (3): 289–299.
  • Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20 37–46.
  • Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46.
  • DigitalGlobe. 2009. White Paper: The Benefits of the 8 Spectral Bands of WorldView–2. Longmont, CO: DigitalGlobe.
  • DigitalGlobe. 2010. DigitalGlobe Core Imagery Products Guide. Accessed October 20, 2013. http://www.digitalglobe.com/downloads/DigitalGlobe_Core_Imagery_Products_Guide.pdf.
  • Dingle, R. L., and D. J. King. 2011. “Comparison of Pixel- and Object-Based Classification in Land Cover Change Mapping.” International Journal of Remote Sensing 32 (6): 1505–1529.
  • 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 (0): 259–272.
  • ENVI-Zoom. 2010. ENVI User Guide. Denver, CO: ITT.
  • Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy.” Photogrammetric Engineering and Remote Sensing 70 (5): 627–634.
  • Göbel, P., C. Dierkes, and W. Coldewey. 2007. “Storm Water Runoff Concentration Matrix for Urban Areas.” Journal of Contaminant Hydrology 91 (1): 26–42.
  • Goodchild, M. F., G. S. Biging, R. G. Congalton, P. G. Langley, N. R. Chrisman, and F. W. Davis. 1994. Final Report of the Accuracy Assessment Task Force. California Assembly Bill AB1580. Santa Barbara: University of California, National Center for Geographic Information and Analysis (NCGIA).
  • Hamedianfar, A., and H. Z. M. Shafri. 2013. “Development of Fuzzy Rule-Based Parameters for Urban Object-Oriented Classification Using Very High Resolution Imagery.” Geocarto International doi:10.1080/10106049.2012.760006.
  • Hay, G., and G. Castilla. 2008. “Geographic Object-Based Image Analysis (GEOBIA): A New Name for a New Discipline.” In Rio De Janeiro Using an Object-Based Approach in: Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, edited by T. Blaschke, S. Lang, and G. J. Hay, 75–89. Berlin: Springer Verlag.
  • Herold, M., M. E. Gardner, and D. A. Roberts. 2003. “Spectral Resolution Requirements for Mapping Urban Areas.” IEEE Transactions on Geoscience and Remote Sensing 41 (9): 1907–1919.
  • Heumann, B. W. 2011. “An Object-Based Classification of Mangroves Using a Hybrid Decision Tree – Support Vector Machine Approach.” Remote Sensing 3 (0): 2440–2460.
  • Hsu, C. W., C. C. Chang, and C. J. Lin. 2009. “A Practical Guide to Support Vector Classification, 2003.” Accessed October 15, 2013. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.
  • Hu, X., and Q. Weng. 2010. “Impervious Surface Area Extraction from IKONOS Imagery Using an Object-Based Fuzzy Method.” Geocarto International 26 (1): 3–20.
  • Hussain, M., D. Chen, A. Cheng, H. Wei, and D. Stanley. 2013. “Change Detection from Remotely Sensed Images: From Pixel-Based to Object-Based Approaches.” ISPRS Journal of Photogrammetry and Remote Sensing 80 (0): 91–106.
  • Jacobson, C. R. 2011. “Identification and Quantification of the Hydrological Impacts of Imperviousness in Urban Catchments: A Review.” Journal of Environmental Management 92 (6): 1438–1448.
  • Jin, X., and S. Paswaters. 2007. “A Fuzzy Rule Base System for Object-Based Feature Extraction and Classification.” Paper presented at the Signal Processing, Sensor Fusion, and Target Recognition. Proc SPIE. 6567:65671H
  • Liu, D., and F. Xia. 2010. “Assessing Object-Based Classification: Advantages and Limitations.” Remote Sensing Letters 1 (4): 187–194.
  • MacFaden, S. W., J. P. M. O’Neil-Dunne, A. R. Royar, J. W. T. Lu, and A. G. Rundle. 2012. “High-Resolution Tree Canopy Mapping for New York City Using LIDAR and Object-Based Image Analysis.” Journal of Applied Remote Sensing 6 (1): 063567.
  • Malinverni, E. S., A. N. Tassetti, A. Mancini, P. Zingaretti, E. Frontoni, and A. Bernardini. 2011. “Hybrid Object-Based Approach for Land Use/Land Cover Mapping Using High Spatial Resolution Imagery.” International Journal of Geographical Information Science 25 (6): 1025–1043.
  • Mantero, P., G. Moser, and S. B. Serpico. 2005. “Partially Supervised Classification of Remote Sensing Images Through SVM-Based Probability Density Estimation.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 559–570.
  • Myint, S. W., P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng. 2011. “Per-Pixel Vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115 (5): 1145–1161.
  • O‘Neil-Dunne, J. P. M., S. W. MacFaden, A. R. Royar, and K. C. Pelletier. 2012. “An Object-Based System for LiDAR Data Fusion and Feature Extraction.” Geocarto International 1–16. doi:10.1080/10106049.2012.689015.
  • Owen, K. K., and D. W. Wong. 2013. “An Approach to Differentiate Informal Settlements Using Spectral, Texture, Geomorphology and Road Accessibility Metrics.” Applied Geography 38 (0): 107–118.
  • Pal, M., and P. M. Mather. 2005. “Support Vector Machines for Classification in Remote Sensing.” International Journal of Remote Sensing 26 (5): 1007–1011.
  • Pinho, C. M. D., L. M. G. Fonseca, T. S. Korting, C. M. de Almeida, and H. J. H. Kux. 2012. “Land-Cover Classification of an Intra-Urban Environment Using High-Resolution Images and Object-Based Image Analysis.” International Journal of Remote Sensing 33 (19): 5973–5995.
  • Qin, Y., Z. Niu, F. Chen, B. Li, and Y. Ban. 2013. “Object-Based Land Cover Change Detection for Cross-Sensor Images.” International Journal of Remote Sensing 34 (19): 6723–6737.
  • Radoux, J., P. Bogaert, D. Fasbender, and P. Defourny. 2010. “Thematic Accuracy Assessment of Geographic Object-Based Image Classification.” International Journal of Geographical Information Science 25 (6): 895–911.
  • Shackelford, A. K., and C. H. Davis. 2003. “A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multispectral Data Over Urban Areas.” IEEE Transactions on Geoscience and Remote Sensing 41 (10): 2354–2363.
  • Slonecker, E. T., D. B. Jennings, and D. Garofalo. 2001. “Remote Sensing of Impervious Surfaces: A Review.” Remote Sensing Reviews 20 (3): 227–255.
  • Stamou, A., P. Patias, A. Papadopoulos, and I. Theodoridou. 2012. “Study and Analysis of WORldVIEW-2 Satellite Imagery for Evaluating the Energy Efficiency of the Urban Area of Kalamaria, Greece.” South-Eastern European Journal of Earth Observation and Geomatics 1 (1): 41–54.
  • Taubenböck, H., T. Esch, M. Wurm, A. Roth, and S. Dech. 2010. “Object-Based Feature Extraction Using High Spatial Resolution Satellite Data of Urban Areas.” Journal of Spatial Science 55 (1): 117–132.
  • Tian, J., and D. M. Chen. 2007. “Optimization in Multi-Scale Segmentation of High-Resolution Satellite Images for Artificial Feature Recognition.” International Journal of Remote Sensing 28: 4625–4644.
  • Vapnik, V. N. 1995. The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Wang, L., G. S. Biging, and P. Gong. 2004. “Integration of Object-Based and Pixel-Based Classification for Mapping Mangroves with IKONOS Imagery.” International Journal of Remote Sensing 25: 5655–5668.
  • Wolf, A. 2010. “Using World View 2 Vis-NIR MSI Imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios.” Accessed July 20, 2013. http://www.exelisvis.com/portals/0/pdfs/envi/8_bands_Antonio_Wolf.pdf.
  • Zhou, W., and A. Troy. 2008. “An Object‐Oriented Approach for Analysing and Characterizing Urban Landscape at the Parcel Level.” International Journal of Remote Sensing 29 (11): 3119–3135.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.