281
Views
10
CrossRef citations to date
0
Altmetric
Articles

Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data

, &
Pages 3586-3607 | Received 14 Jan 2016, Accepted 21 Feb 2017, Published online: 23 Mar 2017

References

  • Arefi, H., and M. Hahn. 2005. “A Morphological Reconstruction Algorithm for Separating Off-terrain Points from Terrain Points in Laser Scanning Data.” International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 36: 3/W19.
  • Baatz, M., and A. Schäpe. 2000. “Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation.” Angewandte Geographische Informationsverarbeitung 12 (12): 12–23.
  • Ban, Y., H. Hu, and I. M. Rangel. 2010. “Fusion of Quickbird MS and RADARSAT SAR Data for Urban Land-Cover Mapping: Object-Based and Knowledge-Based Approach.” International Journal of Remote Sensing 31 (6): 1391–1410. doi:10.1080/01431160903475415.
  • Benz, U. C., P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen. 2004. “Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (3): 239–258. doi:10.1016/j.isprsjprs.2003.10.002.
  • Blaschke, T., G. J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Q. Feitosa, F. van der Meer, H. van der Werff, and F. van Coillie. 2014. “Geographic Object-Based Image Analysis–Towards a New Paradigm.” ISPRS Journal of Photogrammetry and Remote Sensing 87: 180–191. doi:10.1016/j.isprsjprs.2013.09.014.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Brink, A. B., and H. D. Eva. 2009. “Monitoring 25 Years of Land Cover Change Dynamics in Africa: A Sample Based Remote Sensing Approach.” Applied Geography 29 (4): 501–512. doi:10.1016/j.apgeog.2008.10.004.
  • Chini, M., A. Chiancone, and S. Stramondo. 2014. “Scale Object Selection (SOS) through a Hierarchical Segmentation by a Multi-Spectral Per-Pixel Classification.” Pattern Recognition Letters 49: 214–223. doi:10.1016/j.patrec.2014.07.012.
  • Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
  • 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.
  • Ehlers, M., M. Gaehler, and R. Janowsky. 2006. “Automated Techniques for Environmental Monitoring and Change Analyses for Ultra High Resolution Remote Sensing Data.” Photogrammetric Engineering and Remote Sensing 72 (7): 835–844. doi:10.14358/PERS.72.7.835.
  • Espindola, G. M., G. Câmara, 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.
  • Fan, J., D. K. Y. Yau, A. K. Elmagarmid, and W. G. Aref. 2001. “Automatic Image Segmentation by Integrating Color-Edge Extraction and Seeded Region Growing.” IEEE Transactions on Image Processing 10 (10): 1454–1466. doi:10.1109/83.951532.
  • Gamba, P. 2014. “Image and Data Fusion in Remote Sensing of Urban Areas: Status Issues and Research Trends.” International Journal of Image and Data Fusion 5 (1): 2–12. doi:10.1080/19479832.2013.848477.
  • Gao, Y., J. F. 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.
  • Gislason, P. O., J. A. Benediktsson, and J. R. Sveinsson. 2006. “Random Forests for Land Cover Classification.” Pattern Recognition Letters 27 (4): 294–300. doi:10.1016/j.patrec.2005.08.011.
  • Gonzalez, R. C. 2009. Digital Image Processing. Upper Saddle River, NJ: Pearson Education India.
  • Hadavand, A., M. Saadatseresht, and S. Homayouni. 2015. “A New Framework for Object-Based Image Analysis Based on Segmentation Scale Space and Random Forest Classifier.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40 (1): 263. doi:10.5194/isprsarchives-XL-1-W5-263-2015.
  • Han, N., H. Du, G. Zhou, X. Xu, H. Ge, L. Liu, G. Gao, and S. Sun. 2015. “Exploring the Synergistic Use of Multi-Scale Image Object Metrics for Land-Use/Land-Cover Mapping Using an Object-Based Approach.” International Journal of Remote Sensing 36 (13): 3544–3562. doi:10.1080/01431161.2015.1065357.
  • Haralick, R. M., K. Shanmugam, and I. H. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man and Cybernetics (6): 610-621.
  • Hay, G. J. 2014. “Visualizing Scale‐Domain Manifolds: A Multiscale Geo‐Object‐Based Approach.” In Scale Issues in Remote Sensing, edited by Q. Weng, 139–169. Hoboken, NJ: John Wiley & Sons, Inc. doi:10.1002/9781118801628.ch08
  • Hinz, S., and A. Baumgartner. 2003. “Automatic Extraction of Urban Road Networks from Multi-View Aerial Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 58 (1): 83–98. doi:10.1016/S0924-2716(03)00019-4.
  • Ikokou, G. B., and J. Smit. 2013. “A Technique for Optimal Selection of Segmentation Scale Parameters for Object-Oriented Classification of Urban Scenes.” South African Journal of Geomatics 2 (4): 358–369.
  • Imaging, Definiens. 2004. “Ecognition User Guide.” http://www.definiens-imaging.com.
  • Inglada, J., and E. Christophe. 2009. “The Orfeo Toolbox Remote Sensing Image Processing Software.” Paper Presented at the 2009 IEEE International Geoscience and Remote Sensing Symposium, 12–17 July 2009.
  • Jain, A. K., R. P. W. Duin, and J. Mao. 2000. “Statistical Pattern Recognition: A Review.” IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1): 4–37. doi:10.1109/34.824819.
  • 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.
  • Johnson, B. A. 2013. “High-Resolution Urban Land-Cover Classification Using a Competitive Multi-Scale Object-Based Approach.” Remote Sensing Letters 4 (2): 131–140. doi:10.1080/2150704X.2012.705440.
  • Johnson, B. A., M. Bragais, I. Endo, D. B. M. Macandog, and P. B. M. 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.
  • Karadağ, Ö. Ö., C. Senaras, and F. T. Yarman Vural. 2015. “Segmentation Fusion for Building Detection Using Domain-specific Information.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (7): 3305–3315. doi:10.1109/JSTARS.2015.2403617.
  • Langford, E. 2006. “Quartiles in Elementary Statistics.” Journal of Statistics Education 14 (3): n3.
  • Lawrence, R. L., and C. J. Moran. 2015. “The Americaview Classification Methods Accuracy Comparison Project: A Rigorous Approach for Model Selection.” Remote Sensing of Environment 170: 115–120. doi:10.1016/j.rse.2015.09.008.
  • Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28 (5): 823–870. doi:10.1080/01431160600746456.
  • Ma, X., H. Shen, J. Yang, L. Zhang, and P. Li. 2014. “Polarimetric-Spatial Classification of SAR Images Based on the Fusion of Multiple Classifiers.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (3): 961–971. doi:10.1109/JSTARS.2013.2265331.
  • Meinel, G., and M. Neubert. 2004. “A Comparison of Segmentation Programs for High Resolution Remote Sensing Data.” International Archives of Photogrammetry and Remote Sensing 35 (Part B):1097–105.
  • Moser, G., D. Tuia, and M. Shimoni. 2015. “2015 IEEE GRSS Data Fusion Contest: Extremely High Resolution Lidar and Optical Data [Technical Committees].” Geoscience and Remote Sensing Magazine, IEEE 3 (1): 40–41. doi:10.1109/MGRS.2015.2397448.
  • Otsu, N. 1975. “A Threshold Selection Method from Gray-Level Histograms.” Automatica 11 (285–296): 23–27.
  • Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
  • Parece, T. E., and J. B. Campbell. 2015. “Land Use/Land Cover Monitoring and Geospatial Technologies: An Overview.” In Advances in Watershed Science and Assessment, edited by T. Younos and T. E. Parece, 1–32. Cham: Springer.
  • Powers, D. M. 2011. “Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness and Correlation.” Journal of Machine Learning Technology 2: 37–63.
  • Rastiveis, H., F. Samadzadegan, and P. Reinartz. 2013. “A Fuzzy Decision Making System for Building Damage Map Creation Using High Resolution Satellite Imagery.” Natural Hazards and Earth System Sciences 13 (2): 455. doi:10.5194/nhess-13-455-2013.
  • Rottensteiner, F., J. Trinder, S. Clode, and K. Kubik. 2005. “Using the Dempster–Shafer Method for the Fusion of LIDAR Data and Multi-Spectral Images for Building Detection.” Information Fusion 6 (4): 283–300. doi:10.1016/j.inffus.2004.06.004.
  • Samet, H., and M. Tamminen. 1986. “An Improved Approach to Connected Component Labeling of Images.” Proceedings of International Conference on Computer Vision And Pattern Recognition (CVPR’86) 312–318.
  • Santos, J. A. D., P. H. Gosselin, S. Philipp-Foliguet, R. D. S. Torres, and A. X. Falao. 2012. “Multiscale Classification of Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 50 (10): 3764–3775. doi:10.1109/TGRS.2012.2186582.
  • Schwanghart, W., and N. J. Kuhn. 2010. “Topotoolbox: A Set of Matlab Functions for Topographic Analysis.” Environmental Modelling & Software 25 (6): 770–781.
  • Song, C. 2005. “Spectral Mixture Analysis for Subpixel Vegetation Fractions in the Urban Environment: How to Incorporate Endmember Variability?” Remote Sensing of Environment 95 (2): 248–263. doi:10.1016/j.rse.2005.01.002.
  • Sripada, R. P., R. W. Heiniger, J. G. White, and A. D. Meijer. 2006. “Aerial Color Infrared Photography for Determining Early In-season Nitrogen Requirements in Corn.” Agronomy Journal 98 (4): 968–977.
  • Stefanov, W. L., M. S. Ramsey, and P. R. Christensen. 2001. “Monitoring Urban Land Cover Change: An Expert System Approach to Land Cover Classification of Semiarid to Arid Urban Centers.” Remote Sensing of Environment 77 (2): 173–185. doi:10.1016/S0034-4257(01)00204-8.
  • Takayama, T., A. Iwasaki, and O. Kashimura. 2014. “Optimal Segmentation of Classification and Prediction Maps for Monitoring Forest Condition with Spectral and Spatial Information from Hyperspectral Data.” Paper presented at the IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
  • 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. doi:10.1080/14498596.2010.487854.
  • Theodoridis, S., and K. Koutroumbas. 2003. Pattern Recognition. Second Edition ed. San Diego, CA: Academic Press.
  • Tzotsos, A., K. Karantzalos, and D. Argialas. 2014. “Multiscale Segmentation and Classification of Remote Sensing Imagery with Advanced Edge and Scale‐Space Features.” Scale Issues in Remote Sensing, edited by Q. Weng, 170–196. Hoboken, NJ: John Wiley & Sons, Inc. doi:10.1002/9781118801628.ch09
  • Wang, L., Q. Dai, Q. Xu, and Y. Zhang. 2015. “Constructing Hierarchical Segmentation Tree for Feature Extraction and Land Cover Classification of High Resolution MS Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (5): 1946–1961. doi:10.1109/JSTARS.2015.2428232.
  • Wang, Z., J. R. Jensen, and J. Im. 2010. “An Automatic Region-Based Image Segmentation Algorithm for Remote Sensing Applications.” Environmental Modelling Software 25 (10): 1149–1165. doi:10.1016/j.envsoft.2010.03.019.
  • Waser, L. T., E. Baltsavias, K. Ecker, H. Eisenbeiss, E. Feldmeyer-Christe, C. Ginzler, M. Küchler, and L. Zhang. 2008. “Assessing Changes of Forest Area and Shrub Encroachment in a Mire Ecosystem Using Digital Surface Models and CIR Aerial Images.” Remote Sensing of Environment 112 (5): 1956–1968. doi:10.1016/j.rse.2007.09.015.
  • Wilson, J. S., M. Clay, E. Martin, D. Stuckey, and K. Vedder-Risch. 2003. “Evaluating Environmental Influences of Zoning in Urban Ecosystems with Remote Sensing.” Remote Sensing of Environment 86 (3): 303–321. doi:10.1016/S0034-4257(03)00084-1.
  • 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. doi:10.1016/j.isprsjprs.2014.04.008.
  • Yang, J., Y. He, and Q. Weng. 2015. “An Automated Method to Parameterize Segmentation Scale by Enhancing Intrasegment Homogeneity and Intersegment Heterogeneity.” IEEE Geoscience and Remote Sensing Letters 12 (6): 1282–1286. doi:10.1109/LGRS.2015.2393255.
  • 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.
  • Zerrouki, N., and D. Bouchaffra. 2014. “Pixel-Based or Object-Based: Which Approach Is More Appropriate for Remote Sensing Image Classification?” Paper presented at the IEEE International Conference on Systems, Man and Cybernetics (SMC).
  • Zhang, J. 2010. “Multi-Source Remote Sensing Data Fusion: Status and Trends.” International Journal of Image and Data Fusion 1 (1): 5–24. doi:10.1080/19479830903561035.
  • Zhang, Y. J. 1996. “A Survey on Evaluation Methods for Image Segmentation.” Pattern Recognition 29 (8): 1335–1346. doi:10.1016/0031-3203(95)00169-7.
  • Zhao, M., F. Li, and G. Tang. 2012. “Optimal Scale Selection for DEM Based Slope Segmentation in the Loess Plateau.” International Journal of Geosciences 3 (01): 37. doi:10.4236/ijg.2012.31005.
  • Zhong, Y., J. Zhao, and L. Zhang. 2014. “A Hybrid Object-Oriented Conditional Random Field Classification Framework for High Spatial Resolution Remote Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 52 (11): 7023–7037. doi:10.1109/TGRS.2014.2306692.

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.