Object-oriented approach towards a semi-automated classification of urban areas Thomas Esch, Achim Roth, Günter Strunz German Remote Sensing Data Center (DFD) German Aerospace Center (DLR) Oberpfaffenhofen, 82234 Wessling, Germany
[email protected],
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Abstract Urban areas have shown an increasing dynamic of changes within the last decades. To monitor and assess this development planning authorities strongly depend on precise information in shorter update intervals and in more detail than provided by current data sets, e.g. ATKIS or CORINE Land Cover. The paper presents recent developments towards a semi-automated classification of Landsat-7 data, with the goal to support of a more frequently update of the CORINE Land Cover data set within urban areas. The concept is based on a hybrid approach consisting of an object oriented classification, which is followed by a visual interpretation guided by the results of the image analysis. In the paper the method is described in principle and some preliminary results are presented.
1
Introduction
Several studies have been focussing on the use of satellite imagery for the monitoring of urban environments. Change analyses based on spectral indices have been performed e.g. by Ridd & Liu (1998) and Masek et al. (2000). For the extraction of built-up areas, the use of textural information in combination with conventional spectral features has shown very promising results (Steinnocher, 1997; Ryherd & Woodcock, 1996). While most of the textural analysis was based on conventional pixel-based techniques recent studies increasingly focus on approaches based on an object-oriented image analysis (De Kok et al., 2003; Hofmann, 2001; Kressler et al., 2002; Neubert & Meinel, 2002). This study is aiming at the development of a concept for the semi-automated extraction of urban classes according to the nomenclature of the CORINE Land Cover project. The goal of these research activities is to develop methods, which in future shall allow a more efficient and frequent update of the CORINE Land Cover data base with respect to the urban classes. In this paper, first an overview on the methodological concept is given. Then preliminary results of the approach are shown and finally an outlook on further research is given.
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2
Methodology
The methodology is based on a hybrid approach, which consists of −
an object oriented image analysis and classification, and
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visual interpretation, which is supported by the results of the classification.
The image analysis is performed on the basis of an object oriented approach using the software eCognition 2.1 which is based on the Fractal Net Evolution concept developed by Definiens Imaging (Baatz & Schäpe, 1999; Blaschke, 2000; Hofman, 2001). This concept is based on two steps: −
the segmentation and object generation, and
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the rule-based classification of the objects.
In the segmentation and object generation step, the segments are generated on several scale-levels to form a hierarchical network of image objects. The segmentation is determined by the parameters colour and shape which are used to define the degree of homogeneity within the segments. The specific settings are defined interactively and are adapted to the different levels in the hierarchical network. In the next step the knowledge base is created. The rules can be based on spectral and textural characteristics of the objects, the hierarchical context of the segments, and the interaction between neighbouring objects. Based on these rules the image is analysed and classified according to a fuzzy logic or a nearest neighbour algorithm. Figure 1 shows the structure of the hybrid approach to derive CORINE Land Cover classes of urban areas. Starting with the segmentation of the image, which is based on spectral and textural parameters, several hierarchical segmentation levels are generated. The number of hierarchical levels has to be adapted to the spatial characteristics of the land use patterns. At next the knowledge base is developed. This classification scheme includes an initial land cover analysis. The category “built-up areas” is classified on the basis of spectral and textural parameters as well as contextual (neighbouring) and hierarchical (relation between segmentation levels) characteristics. Moreover, for the urban areas the degree of sealed surface within each segment is determined by a calculation of the scene-dependant,
Multispectral image Texture layer
1. Segmentation 2. Knowledge base Segmentation levels
Features:
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Colour Texture Context Hierarchy
3. Classification
Corine land cover map Accuracy map
4. Interpretation
Fig. 1:
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Flow chart of the semi-automated approach
linear relationship between the degree of surface sealing and the value of the Normalized Difference Vegetation Index (NDVI). This technique represents a slightly modified implementation of the basic approach presented by Netzband (1998) and is described more detailed in Esch et al. (2003). Subsequently, the land use is classified on the basis of both, the land cover analysis and the mapping of the degree of sealing. Additional rules are applied to take into account supplementary contextual and class-hierarchical characteristics as well as the degree of sealing to derive land use classes from the land cover categories. The classification result is then given to the interpreter along with additional information on the classification accuracy for each object: Based on this information the interpreter can control and revise critical class assignments or classify those categories manually that can not be identified automatically.
3
Practical results
The approach has been applied to a study area, which is part of the Rhine-Neckar region in Germany, using a Landsat 7 ETM+ scene recorded at 15th August 2001. The object oriented classification approach was used to derive the “Artificial Surfaces” classes of CORINE Land Cover shown in Fig. 2. The land cover analysis yields an accuracy of 90 % for the build-up areas. Problems mainly occurred in the context of confusions between highly textured agricultural fields situated along the exterior contour line of the settlements (see Fig. 3). These difficulties are mainly due to spectral and textural ambiguities. The degree of sealing has been classified with an average deviation of 8% and a maximum deviation of 17% respectively (see Fig. 4). This accuracy is calculated by a comparison with interpretation from aerial photographs. The overall accuracy of the CORINE Level 1 category “Artificial areas” is about 90%. ReFig. 2: CLC classes “Artificial Surfaces” garding CORINE level 2 the categories “Urban fabric”, “Industrial, commercial, and transport units” and the “Artificial vegetated areas” are mapped satisfactorily. Only the detection of “Roads and rail networks” involves significant difficulties due to their small sizes in relation to the spatial resolution of the Landsat data. The classification of “Mine, dump, and construction sites” is limited to the Level 3 subdivision “Construction sites” and even these can only be detected properly within built-up areas. With respect to CORINE Level 3 the degree of surface sealing provides very valuable information. By using this information the differentiation of the “Urban fabric” into “Continuous urban fabric” and “Discontinuous urban fabric” is possible. However, difficulties occur in the discrimination between spectrally similar city centres and commercial sites (see Fig. 5). “Industrial, commercial and public units” can be addressed satisfactorily as their textural and spectral characteristics are quite specific. The identification of “Road and rail networks” is problematical due to the spatial resolution and consequently the results are rather poor. The classifi-
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cation of port areas is difficult, because they can hardly be separated from industrial and commercial units. The use of information on the adjacency to a river or the existence of port basins (see Fig. 5) can improve the results. “Green urban areas” can be classified accurately with the help of neighbourhood-related information. In contrary the “Sport and leisure facilities” have to be classified manually as they cannot be described by solid features.
Fig. 3: Ambiguities between built-up area and bare fields
non-built-up area
Fig. 4.
Fig. 5:
4
Map showing the degree of sealing
CORINE land cover classification of Ludwihafen city centre (yellow circle: city centre ; red circle: industrial unit)
Conclusions
The concept for a semi-automated classification of urban areas based on an object-oriented approach has shown promising results. However, these results are preliminary and require further research. Current research and development activities are focusing on the improvement of the interactive parameter settings (e.g. for segmentation and number of hierarchical levels) and the knowledge base as well as the refinement of the interface between the semiautomatic classification and interactive part of the concept. The main focus of future research and development is put on the transferability of the approach and the adaptation to different regions within Germany and Europe.
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References BAATZ, M., SCHÄPE, A. (1999): Object-Oriented and Multi-Scale Image Analysis in Semantic Networks. In: Proc. of the 2nd International Symposium on Operationalization of Remote Sensing, August 16-20, 1999. ITC, Enschede. BAUER, T., STEINNOCHER, K. (2001): Per-parcel land use classification in urban areas applying a rule-based technique, In: GeoBIT/GIS, 6(2001), 24-27. BLASCHKE, T. (2000): Objektextraktion und regelbasierte Klassifikation von Fernerkundungsdaten: Neue Möglichkeiten für GIS-Anwender und Planer. In: 5. Symposium „Computergestützte Raumplanung” – CORP2000: 153-162 DE KOK, R., WEVER, T., FOCKELMANN, R. (2003): Analysis of urban structure and development applying procedures for automatic mapping of large area data. In: Carstens, J. (Ed.): Remote Sensing of Urban Areas 2003, 41-46. ESCH, T., ROTH, A., STRUNZ, G., DECH, S. (2003): Object-oriented classification of Landsat-7 data for regional planning purposes. In: Carstens, J. (Ed.): Remote Sensing of Urban Areas 2003, 50-55. HOFMAN, P. (2001): Detecting urban features from IKONOS data using an object oriented approach. In: Remote Sensing & Photogrammetry Society (Ed.): Proceedings of the First Annual Conference of the Remote Sensing & Remote Sensing Society, 28–33. KRESSLER, F., STEINNOCHER, K, KIM, Y. (2002): Urban land cover mapping from Kompsat EOC panchromatic images using an object-oriented classification approach. In: Proceedings of the Third International Symposium Remote Sensing of Urban Areas, Vol. 1, ISBN 975-567-219-X, pp. 219-226, Istanbul, 11-13 June, 2002. MASEK, J.G., LINDSAY, FE., GOWARD, S.N. (2000): Dynamics of urban growth in Washington DC metropolitan area 1973-1996 from Landsat observations. International Journal of Remote Sensing, 21(18), 3473-3486. NETZBAND, M. (1998): Möglichkeiten und Grenzen der Versiegelungskartierung in Siedlungsgebieten. IÖR-Schriften No. 28, Dresden. NEUBERT, M., MEINEL, G. (2002): Fortführung von Geobasisdaten durch die segmentbasierte Auswertung von IKONOS-Daten: erste Ergebnisse. In: Strobl, J., Blaschke T., Griesebner, G. (Ed.): Angewandte Geographische Informationsverarbeitung XIV. Beiträge zum AGIT-Symposium Salzburg 2002, Wichmann, Heidelberg, 403-408. RIDD, M.K., LIU, J. (1998): A comparison of four algorithms for change detection in an urban environment. Remote Sensing of Environment. 63, 95-100. RYHERD, S., WOODCOCK, C. (1996): Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images. Photogrammetric Engineering and Remote Sensing, vol. 62, no. 2, 181-194. STEINNOCHER, K. (1997): Texturanalyse zur Detektion von Siedlungsgebieten in hochauflösenden panchromatischen Satellitenbilddaten. In: Dollinger, F., Strobl. J. (Hrsg.): Angewandte Geographische Informationsverarbeitung IX, Salzburger Geographische Materialien, no. 26, 143-153.
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