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Monitoring residential areas at a regional scale, and even at a global scale, has become an increasingly important topic.
However, extraction of residential information was still a difficulty and challenging task, such as multiple usable data
selection and automatic or semi-automatic techniques. In metropolitan area, such as Beijing, urban sprawl has brought
enormous pressure on rural and natural environments. Given a case study, a new strategy of extracting of residential
information integrating the upscaling methods and object multi-features was introduced in high resolution SPOT fused
image. Multi-resolution dataset were built using upscaling methods, and optimal resolution image was selected by
semi-variance analysis approach. Relevant optimal spatial resolution images were adopted for different type of
residential area (city, town and rural residence). Secondly, object multi-features, including spectral information, generic
shape features, class related features, and new computed features, were introduced. An efficient decision tree and Class
Semantic Representation were set up based on object multi-features. And different classes of residential area were
extracted from multi-resolution image. Afterwards, further discussion and comparison about improving the efficiency
and accuracy of classification with the proposed approach were presented. The results showed that the optimal resolution
image selected by upscaling and semi-variance method successfully decreased the heterogeneous, smoothed the noise
influence, decreased computational, storage burdens and improved classification efficiency in high spatial resolution
image. The Class Semantic Representation and decision tree based on object multi-features improved the overall
accuracy and diminished the 'salt and pepper effect'. The new image analysis approach offered a satisfactory solution for
extracting residential information quickly and efficiently.
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