Challenges in Object-Oriented Analysis for Intra-Urban Land Cover Classification from High Resolution Images: A Brazilia

1. Intra-Urban Land Cover Classification in High Spatial Resolution Images using Object-Oriented Analysis: trends and challenges Carolina Moutinho Duque de Pinho carolina@dpi.inpe.br 1st Multidisciplinary workshop on extracting and classifying urban objects from high resolution satellite images April, 18, 2007

2. Introduction • What is the importance in classifying land cover on such a detailed scale of urban areas? • Impervious soil mapping  surface run-off and flood studies in urban areas. • Use this information for the analysis of urban micro-climate. • Studies on urban vegetation  urban greening maps of town neighborhoods. • Act as a initial stage for land use classification processes.

3. Introduction • The object-oriented analysis is applied in l intra-urban land cover classification in in many cities of the world . • Many of them have reached a very good thematic accuracy. • But is necessary to point out some issues in these researches: • They have been using a few number of class • classification systems less complex  thus the possibilities of errors decreases. • Many of them were realized in well planning cities, European and American. • In those cities there are few numbers of spatial patterns well defined. • Often, the differences among the patterns inside the test area are not very big. • Generally, they use a very small area of study, many times the area is restrict a couple of quarters. Thus, It has a less amount of problems with computer processing capacity.

4. Prupose • This presentation is committed to show the shortcomings and alternatives in intra-urban land cover classification using high resolution images, specially in Brazilian cities, where the urban planning and management have not been able to control the urban sprawl.

5. Test Area SÃO JOSÉ DOS CAMPOS TEST AREA (12 km2) SÃO PAULO STATE

6. Experiments • Experiment I  carried out for a complex intra-urban setting; • classification scheme has been conceived and further applied to the whole study area, a highly complex and heterogeneous environment. • Experiment II  accomplished for a smaller intra-urban area. • The goal was to evaluate the influence of urban occupation on the performance of land cover classification. • Five quarters of Sao José dos Campos with different spatial patterns were selected.

7. Experiments • Data: • Merged Quickbird image of may, 2004 are used; • Vector data of blocks to restrict the occurrence of built areas land cover classes; • Vector data of quarters; • IHS composition from natural color image. • Software • Envi 4.0 for pre-processing tasks • E-Cognition 4.0 for object-oriented image analysis.

8. Experiment I - Segmentation Set of parameters to Keep shape? Set of parameters to Keep spectral information?

9. Difficulties in Experiment I • It was difficult to choose a set of segmentation parameters that match to all of the spatial patterns in the test area. • Thus I have chosen between keep the shape of the manmade objects from well-organized quarters or keep the spectral information from the manmade objects and “natural objects” from disorganized quarters. I have chosen the second option. • Results: • A very large number of objects (approximately 400.000)create problems with computer processing limitations: • It has hindered the re-segmentation operations. • It has calculated slowly the sample histograms • The limits of created objects did not translate the shape of them. Thus, I could not use the shape attributes to do the semantic net.

10. Experiment - I – The Semantic Net objects 11 classes Vegetation Non-Vegetation Shadow Non-Shadow Trees Grass Brighter objects (light types of concrete; some cars; metallic roofs Non- Brighter objects Coloreds Non-Coloreds Swimming pool Bluish Brown Light Red Medium Concrete Dark objects Metallic roofs Light Ceramics Light Bare Soil Dark Bare Soil Dark Ceramics Asphalt Dark Concrete Ceramics Asphalt Pavement Bare Soil Error Asphalt Real Dark Concrete

11. Brighter Objects Ceramics Bare Soil Metallic Roofs Medium concrete Dark Concrete Asphalt Swimming Poll Shadow Trees Grass Non-Classified Objects Concreto / Amianto Médio Exp. I - Classification

12. Kappa per class

13. It has had the better result because swimming pool is so different from the other classes (color cyan with always rectangular shape). Kappa per class

14. Kappa per class It has had the worst result. It has been confused with almost all of classes. It will be necessary to redefine the Medium Concrete scope and characteristics.

15. Kappa per class It has been confused with Dark Concrete, Asphalt and Trees. It was a result of error interpretations in reference polygon. It has been difficult to the interpreter to find visually the color differences among the three classes.

16. Kappa per class It was confused with Dark Concrete, Asphalt an Trees. It was a result of error interpretations in reference polygon. It was so difficult to the interpreter find visually the color differences among the three classes. There has been a confusion between these two classes because They has the same color and it was not possible to use the shape of the buildings (the segmentation problem). Using a DSM, we would resolve this problem.

17. Kappa per class It was confused with Dark Concrete, Asphalt an Trees. It was a result of error interpretations in reference polygon. It was so difficult to the interpreter find visually the color differences among the three classes. There has been a confusion between these two classes because They has the same color and it was not possible to use the shape of the buildings (the segmentation problem). Using a DSM, we would resolve this problem.

18. Kappa per class This class has been confused with Shadows and specially with Grass. The spectral characteristics were not sufficient to distinguish the classes, because of poor spectral resolution of the sensor. The alternative may be the texture.

19. Experiment II • it has chosen a specific set of segmentation parameters for each Quarter; • I could do re-segmentation operations; • The shape of objects are better than the first Experiment ; • It has built a specific semantic net for each quarter. Selected Quarters

20. Experiment II • “Well - organized” Quarters  The objects are regularly disposed in the urban space; homogeneous size and type of roof material. Jardim Renata, Cidade Jardim e Jardim Apolo Selected Quarters

21. Experiment II • “Well - organized” Quarters  The objects are regularly disposed in the urban space; homogeneous size and type of roof material. Jardim Renata, Cidade Jardim e Jardim Apolo Selected Quarters Selected Quarters

22. Experiment II • “Well - organized” Quarters  The objects are regularly disposed in the urban space; homogeneous size and type of roof material. Jardim Renata, Cidade Jardim e Jardim Apolo Selected Quarters

23. Experimento II Example of a “Well - organized” Quarter  Cidade Jardim

24. Experiment II • “Well - organized” Quarters  The objects are regularly disposed in the urban space; homogeneous size and type of roof material. Jardim Renata, Cidade Jardim e Jardim Apolo • “Disorganized” Quarters  heterogeneous size and type of roof material; occurrence of very small objects; the objects are irregularly disposed in the urban space; bigger number of land cover classes than in Well-organized Quarters. Vila Acácias e Vila Letônia Selected Quarters

25. Experiment II • “Well - organized” Quarters  The objects are regularly disposed in the urban space; homogeneous size and type of roof material. Jardim Renata, Cidade Jardim e Jardim Apolo • “Disorganized” Quarters  heterogeneous size and type of roof material; occurrence of very small objects; the objects are irregularly disposed in the urban space; bigger number of land cover classes than in Well-organized Quarters. Vila Acácias e Vila Letônia Selected Quarters

26. Experimento II Example of a “disorganized” Quarter  Vila Letônia

27. Exp. II – Results (Thematic Accuracy) • All of the quarters had better accuracy than the Experiment I expect Vila Letônia Accuracy Complex

28. Conclusions • The characteristics of cities in development countries brings different challenges in land cover classification; • The urban occupation is not “well-organized”. • It is difficult to establish a set of segmentation parameters that works for the whole city. • It is recommended to divide de city in homogenous areas (can be the quarters) with specific segmentations and classification parameters. • Thus, it will be possible to keep the shape attributes and use re-segmentation operations. • Larger test areas demands better software and computers. • The poor spectral resolution of the Quickbird sensor could be overcoming by using DSMs to distinguish built classes (Ceramic and Dark Concrete) from classes with the same color (Bare Soil and Asphalt, respectively). • Trees X Grass  Texture attributes may be a solution.

29. Thank you very much!

30. Exp. I – Confusion Matrix