基于双路细节关注网络的遥感影像建筑物提取

Building Extraction Based on Dual-Stream Detail-Concerned Network

房屋等建筑物的分布情况是衡量地区发展的重要指标,利用遥感影像实现建筑物的自动高精度提取在指导城乡规划和市镇建设等方面具有重要意义。已有方法大多忽略了像素数较少的小面积建筑和边缘等细节信息的处理,针对此问题,提出了一种双路细节关注网络,将语义特征与细节关注特征双路并行优化,进一步提高了遥感影像中建筑物的提取精度。所提方法首先使用双路特征提取模块获取语义特征与细节关注特征,并在解码过程中进行双向优化,增强语义特征细节的同时提高细节关注特征的连续性与类别准确性,然后对二者进行融合,结合细节关注损失的监督,实现建筑物的高精度提取。在WHU建筑物数据集、ISPRS Vaihingen数据集与某地区国产高分数据集上,将所提方法与多种主流方法进行了对比验证,所提方法的F1分数和交并比均高于对比方法,且提取的建筑物完整性更好,小面积建筑漏检、误检率更低。

Objectives:The distribution of buildings is an important indicator to measure regional development.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 network(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 features.Then,a semantic-detail fusion module is used to fuse and squeeze two refined features.Furthermore,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 evaluation 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 maintains 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.