软件导刊2024，Vol.23Issue(12)：189-197,9.DOI:10.11907/rjdk.241061
面向多尺度与条形特征的道路提取方法
Road Extraction Method Oriented by Multi-Scale and Strip Features
摘要
Abstract
In the task of extracting roads from remote sensing images,road information is often affected by environmental factors such as light-ing,shadows,and occlusion,and roads usually appear as slender strips,making it difficult to accurately detect.To this end,an improved LinkNet model(MSS LinkNet)for multi-scale and strip features is proposed to capture contextual information at different scales,which is highly compatible with the slender characteristics of roads.Firstly,the multi-scale convolutional attention module is used as the basic compo-nent unit of the encoder to ensure the model's ability to extract multi-scale and stripe features.Secondly,an improved hollow space pyramid pooling module is added to the central area of the network to enhance the model's ability to parse multi-scale information.Finally,a bar pool-ing module is added to the decoder section to enhance the model's ability to parse bar information.The experiment shows that compared to D-LinkNet,the proposed model has improved IOU by 2.53%and 0.71%on the DeepGlobe and Massachusetts datasets,respectively,while only accounting for 54.15%and 79.63%of D-LinkNet in terms of parameter and computational complexity.关键词
道路提取/多尺度特征/条形特征/注意力机制Key words
road extraction/multi-scale feature/strip feature/attention mechanism分类
信息技术与安全科学引用本文复制引用
沈国治,余瀚,孙明皓,吴彬,龙显忠..面向多尺度与条形特征的道路提取方法[J].软件导刊,2024,23(12):189-197,9.基金项目
国家自然科学基金项目(12371440) （12371440）
南京邮电大学校级自然科学基金项目(NY222140) （NY222140）
