Environmental Engineering Reference
In-Depth Information
9
An object-oriented pattern recognition
approach for urban classification
SoeW.MyintandDouglasStow
In contrast to subpixel and per-pixel image classification approaches, object-based image analysis (OBIA) attempts
to exploit spatially and spectrally similar groups of pixels in order to identify objects within an imaged scene.
Object-based approaches are most applicable to high spatial resolution data, where objects of interest are larger
than the ground resolution element. Such objects in urban scenes could be related to natural features of urban
landscapes (e.g., trees and lakes) or man-made features (e.g., buildings or roads). This chapter introduces readers to
the principles of OBIA and demonstrates how it can be applied to achieve satisfactory accuracy in urban mapping.
We employed two case studies with two example subsets extracted from Quickbird multispectral satellite data and
demonstrated two object-based analysis procedures, namely decision rule (i.e., membership function) and nearest
neighbor classifiers. The object-oriented classification employed to identify urban classes in this chapter is
specifically based on Definiens software and the various routines it contains to achieve OBIA. However, an overview
of the object-oriented approach, parameters generally available for object analysis, image segmentation procedure,
rule set approaches, nearest neighbor classifier using training samples, and limitations/uncertainties associated with
object-based techniques reported in this chapter are applicable to urban mapping using any OBIA software.
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