小组新闻

小组成员张卓尔的论文《基于双路细节关注网络的遥感影像建筑物提取》获得武汉大学学报（信息科学版）》2024年度优秀论文评奖。

论文链接：http://ch.whu.edu.cn/article/doi/10.13203/j.whugis20220613

Abstract: 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.