Extraction of residential information from high-spatial resolution image integrated with upscaling methods and object multi-features

摘要

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 imaue. 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 accuiacy 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.