Building Extraction Based on Dual-Stream Detail-Concerned Network
Objectives:The distribution of buildings is an important indicator to measure regional develop-ment.Automatic extraction of building information from remote sensing images is of great significance for urban and rural planning.Most existing methods underestimate building details such as boundaries and tiny buildings.Methods:In order to increase attention to building details,a dual-stream detail-concerned net-work(DSDCNet)is proposed in an encoder-decoder manner.First,a dual-stream feature extraction module is used to extract semantic features and detail-concerned features.They are fed into the decoder consisting of a series of detail refinement modules where detail-concerned features make up for the missing details of semantic features and the semantic features enhance semantic continuity of detail-concerned fea-tures.Then,a semantic-detail fusion module is used to fuse and squeeze two refined features.Further-more,deep supervision is conducted and the multi-level outputs are used in detail-concerned loss function so as to strengthen the supervision of building details.Results:Five mainstream networks are selected for comparison in WHU dataset,ISPRS Vaihingen dataset and a domestic high-resolution dataset.The evalu-ation results show that DSDCNet has better performance than other networks,especially in F1-score and intersection over union without introducing too much network complexity.Conclusions:DSDCNet not only manages to improve the overall performance of building extraction results,but also effectively main-tains the integrity of building boundaries and reduces the missed detection of small buildings.It has better extraction effect on the buildings with small size and complex context.
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