An Enhanced Morphological Building Index for Building Extraction from High-Resolution Images

Abstract

Abstract:

High-resolution images are important basic data for urban surface features coverage analysis. This study proposed an enhanced morphological building index (EMBI) for automatic building extraction from high-resolution remotely sensed imagery. Firstly we extracted the urban impervious feature, and then EMBI was built based on a multi-scale white top-hat morphology reconstruction operation on the feature, which taking advantage of the relationship between the physical properties of buildings and morphological operators. Subsequently, the EMBI feature image combined with the shape characteristics (length-width ratio, area, etc.) completed the final building extraction using a decision tree method. In order to verify the proposed method, the Washington Commercial Street high-resolution hyperspectral HYDICE image and Wuhan Hongshan District two QuickBird images were used. In these experiments, the EMBI algorithm achieved satisfactory results and outperformed the MBI algorithm in terms of accuracies, i.e. the overall accuracy respectively increased by 7.31%, 6.48%, 7.83%, which proved that the EMBI algorithm performed more reliability.

Key words: high-resolution images, enhanced morphological building index, urban impervious feature, the shape characteristics, decision tree

CLC Number:

P237

References

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