Comparison of Pixel-Based and Object-Oriented Classification Approaches in Arid Urban Areas

1. Comparison between pixel-based and object-oriented classification approaches in urban area of the arid environment Qian Jinga,b, ZhouQiminga, HouQuana a Department of Geography, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong b Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences

2. Outline • Introduction • Study area and data • Methods • Results and discussion • Conclusion International Conference on China's Urban Land and Housing in the 21 Century

3. Introduction • Urban development is one of the major forces causing environmental change in aridzone of China. • In Xinjiang, expansion of urban areas is concentrated within limited space of oases, with constraints such as water resources. • With the rapid increasing population, the expansion of built-up areas are accelerated in the past decades. International Conference on China's Urban Land and Housing in the 21 Century

4. Introduction • Delineating built-up areas from its background has been a constant challenge in remote sensing image processing (Erbek et al., 2004; Lo and Choi, 2004). • With the increasing availability of high-resolution imagery, research has been focused on automated delineation of built-up areas using the images that have high frequency spatial variance with limited spectral resolution. • Object-oriented approach has been developed for the segmentation of images for this. International Conference on China's Urban Land and Housing in the 21 Century

5. The issues addressed • In aridzone of China, the built-up areas are often surrounded by farmland. • However, they may also confuse with nearby bare soil and stony desert, which present very similar spectral characteristics as construction materials such as concrete. • The traditional pixel-based classification typically yield large uncertainty in the classification results. International Conference on China's Urban Land and Housing in the 21 Century

6. Features in arid area Built-up areas represented on the Landsat ETM+ image show different types of cities with significant spatial and spectral variations on the images. Also notice the spectral similarity between bare ground (river bed) and small cities and settlements. International Conference on China's Urban Land and Housing in the 21 Century

7. Objectives • To find the most appropriate approach for auto-classification of built-up areas for the aridzone of China. • Constraints: • Large areas to be covered in a short period time. • Limited availability of high-resolution images. • Cost-benefit concern • Comparison between different classification approaches is addressed by this paper. International Conference on China's Urban Land and Housing in the 21 Century

8. Study area and data Landsat ETM+ image of the study area, acquired on 7 August 2000). Map of the study area: the Centre Town of Manas County, City of Shihezi and part of regimental farm of Division 8, at North Xinjiang Economic Zone, China International Conference on China's Urban Land and Housing in the 21 Century

9. Study area and data • Centered at the city of Shihezi at north slope of Tianshan Mountain, Xinjiang Uygur Autonomous Region of China. • At the centre of a large oasis. • Three types of cities and settlements are identified: • Shihezi: major city in the region, population ~200,000 • Manas: county centre, population ~20,000 • Liangzhouhu town: settlement, population ~ 5000 • Landsat ETM+ image is used, acquired on 7 August 2000 International Conference on China's Urban Land and Housing in the 21 Century

10. Data pre-processing • Geometric correction: image-to-image registration using a geo-coded SPOT Pan image of 2002 as master image, with 37 Ground Control Points. The RMSE is less than 0.5 pixels • Reference data: interpreted from aerial photos acquired in 2000 International Conference on China's Urban Land and Housing in the 21 Century

11. Image classification approaches International Conference on China's Urban Land and Housing in the 21 Century

12. Methods tested • Normalized Difference Built-up Index (NDBI) • Maximum Likelihood Classifier (MLC) • Object-oriented (O-O) image analysis International Conference on China's Urban Land and Housing in the 21 Century

13. NDBI • A pixel-based approach • Using similar concept of vegetation index by delineating built-up areas from other background categories, only two bands of spectral data are used. • The test classification attempts to delineate built-up areas & barren soil, water-bodies and vegetation NDBI = (TM5-TM4) / (TM5+TM4) International Conference on China's Urban Land and Housing in the 21 Century

14. MLC • A pixel-based approach • Attempts to find clusters in the N-dimensional spectral space defined by N bands of spectral data • ENVI/IDL was used for the test. International Conference on China's Urban Land and Housing in the 21 Century

15. Classification scheme • build-up area: mixture of urban areas, settlement or lands under construction; • cropland: cropland or fallow; • garden plot: orchards, vineyards or nurseries; • sparse woodland: low coverage mixture of shrub, desert scrub or bare ground; • dense woodland: high coverage mixture of forest, shrub or shelter belt; • grassland: pasture or desert grass; • river flat: dry river bed or river flat; • water body: reservoirs or fish ponds. International Conference on China's Urban Land and Housing in the 21 Century

16. O-O Image Analysis • Object-oriented approach • Use both spectral and spatial information • eCognition software was used for the test International Conference on China's Urban Land and Housing in the 21 Century

17. Unclassified ETM+ image Object oriented image analysis Referring aerial photos Image segmentation Building knowledge base Training samples selection Classify image with Nearest Neighbour (NN) classifier Classified image in eCognition Accuracy assessment O-O classification method International Conference on China's Urban Land and Housing in the 21 Century

18. Accuracy assessment • Stratified random sampling • Referring to the ortho-corrected aerial photos. • Totally 900 reference sites are selected as ground reference points. • Error matrices were created for MLC and O-O results. International Conference on China's Urban Land and Housing in the 21 Century

19. Results • Classification results from different approaches • The comparison of accuracy of different classification International Conference on China's Urban Land and Housing in the 21 Century

20. Result: NDBI The sparse woodland, bare ground and dry riverbed are merged into the same land-cover class as the background of built-up area. International Conference on China's Urban Land and Housing in the 21 Century

21. Result: MLC International Conference on China's Urban Land and Housing in the 21 Century

22. Result: O-O International Conference on China's Urban Land and Housing in the 21 Century

23. Error matrix - MLC Overall Classification Accuracy = 70.89% Overall Kappa Statistics = 0.6633 International Conference on China's Urban Land and Housing in the 21 Century

24. Error matrix – O-O Overall Classification Accuracy = 89.33% Overall Kappa Statistics = 0.8773 International Conference on China's Urban Land and Housing in the 21 Century

25. Discussion: Comparison between MLC & O-O International Conference on China's Urban Land and Housing in the 21 Century

26. Discussion: usability of NDBI • The NDBI method is found to be unable to differentiate urban areas from the background features such as sparse woodland, bare ground and dry riverbed in arid regions. • The usability of such a pixel-based spectral classifier is severely limited in the arid regions mainly due to the common presence of land-covers of bare ground and dry riverbed, which have similar spectral response with built-up areas. International Conference on China's Urban Land and Housing in the 21 Century

27. Discussion: MLC versus O-O • The object-oriented classifier yields significantly better overall accuracy than the MLC method. • For built-up areas, however, the difference between MLC and O-O methods is less significant. • Both methods appears to have less omission errors but larger commission errors. International Conference on China's Urban Land and Housing in the 21 Century

28. Discussion: shortfalls of the O-O method • The classification accuracy depends on the quality of image segmentation. If objects are extracted inaccurately, subsequent classification accuracy will not improve. • Classification error could be accumulated due to the error in both image segmentation and classification process. • Once an object is misclassified, all pixels in this object will be misclassified. International Conference on China's Urban Land and Housing in the 21 Century

29. Conclusion • This study has compared classifiers regarding to built-up area delineation in aridzone of China. • Although the overall accuracy of the O-O approach is significantly better than that of MLC, there is less significant difference for the built-up area class. • Further research will be focused on the impact of spatial resolution of images and the efficiency of different classifiers. International Conference on China's Urban Land and Housing in the 21 Century

30. Thanks International Conference on China's Urban Land and Housing in the 21 Century