Building Detection Method Considering Multi-scale Features and Global Context
Building extraction from remote sensing images is a semantic segmentation task.However,the local detail information may lost in the encoding stage.The perception ability of global context and the extraction of multi-scale features are insufficient,resulting in the omission of small buildings,incomplete extraction of buildings and internal holes.To solve the above problems,we propose a method considering global context and multi-scale features for building extraction.The method adopts an encoder-decoder structure and contains two core modules.One is the multiscale feature encoding module,which uses parallel continuous dilated convolution to extract multi-scale features.The other is the global context-aware module,which consists of the squeeze and excitation module and the strip pooling module,and is used to obtain sufficient global context information from the channel dimension and spatial dimension.Experimental results on the WHU building dataset and Inria aerial image labeling dataset indicate that the proposed method solves the problems of small buildings omission,incomplete extraction and internal holes while improving the accuracy.
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