Abstract
In an Object Based Image Analysis Classification (OBIA) process, the quality of the classification results are highly dependent on segmentation. However, a high number of the studies that make use of an OBIA process find the segmentation parameters by making use of trial-and-error methods. It is clear that a lack of a structured procedure to determine the segmentation parameters produces unquantified errors in the classification. This paper aims to quantify the effects of using a semi-automatic approach to determine optimal segmentation parameters. To this end, an OBIA process is performed to classify land cover types produced by both a manual and an automatic segmentation. Even though the classification using the manual segmentation outperforms the automatic segmentation, the difference is only 2%. Since the automatic segmentation is performed with optimal parameters, a procedure to accurately determine those parameters must be performed to minimize the error produced by a misjudgment in the segmentation step.
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References
Hay, G., Castilla, G.: Object-based image analysis: Strengths, weaknesses, opportunities and threats (SWOT). In: Proc. 1st Int. Conf. OBIA (2006)
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 Sensing of Environment 127, 357–369 (2012)
Gao, Y., Mas, J.: A Comparison of the Performance of Pixel Based and Object Based Classifications over Images with Various Spatial Resolutions. Online Journal of Earth Sciences (8701) (2008)
Whiteside, T.G., Boggs, G.S., Maier, S.W.: Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation 13(6), 884–893 (2011)
Hantson, W., Kooistra, L., Slim, P.A.: Mapping invasive woody species in coastal dunes in the Netherlands: a remote sensing approach using LIDAR and high-resolution aerial photographs. Applied Vegetation Science 15(4), 536–547 (2012)
Tzotsos, A., Argialas, D.: A support vector machine approach for object based image analysis. In: …Conference on Object-Based Image Analysis …(Negnevitsky) (2006)
Yu, Q., Gong, P., Clinton, N.: Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. …Remote Sensing 72(7), 799–811 (2006)
Navulur, K.: Multispectral Image Analysis Using the Object-Oriented Paradigm. CRC Press (2007)
Blaschke, T.: Object-based contextual image classification built on image segmentation. Advances in Techniques for Analysis of Remotely …00(C) (2003)
Baatz, M., Schäpe, A.: Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische …(2000)
Trimble: eCognition Developer 8.8 Reference Book (2012)
Trimble: eCognition Developer 8.8 User Guide (2012)
Liu, D., Xia, F.: Assessing object-based classification: advantages and limitations. Remote Sensing Letters 1(4), 187–194 (2010)
Möller, M., Lymburner, L., Volk, M.: The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation 9(3), 311–321 (2007)
Gahegan, M.: Is inductive machine learning just another wild goose (or might it lay the golden egg)? International Journal of Geographical Information Science 17, 69–92 (2003)
Atkinson, P.M., Tatnall, A.R.L.: Introduction Neural networks in remote sensing. International Journal of Remote Sensing, 37–41 (2010, October 2012)
Melgani, F., Bruzzone, L.: Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing 42(8), 1778–1790 (2004)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 297, 273–297 (1995)
Johnson, B., Xie, Z.: Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS Journal of Photogrammetry and Remote Sensing 66(4), 473–483 (2011)
QuantumGIS: Quantumgis (August 2013), http://www.qgis.org/
scikit learn: scikit-learn (August 2013), http://scikit-learn.org/
Python: Python (August 2013), http://www.python.org/about/
PostgresSQL: Postgressql (August 2013), http://www.postgresql.org/docs/9.2/interactive/index.html
PostGIS: Postgis (August 2013), http://postgis.net/
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 Journal of Photogrammetry and Remote Sensing 58(3-4), 239–258 (2004)
Zhan, Q., Molenaar, M.: Quality assessment for geo-spatial objects derived from remotely sensed data. …Journal of Remote Sensing 26(14), 2953–2974 (2005)
Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66(3), 247–259 (2011)
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Auquilla, A., Heremans, S., Vanegas, P., Van Orshoven, J. (2014). A Procedure for Semi-automatic Segmentation in OBIA Based on the Maximization of a Comparison Index. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8579. Springer, Cham. https://doi.org/10.1007/978-3-319-09144-0_25
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DOI: https://doi.org/10.1007/978-3-319-09144-0_25
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