摘要
【目的】针对现有遥感影像建筑物提取模型中因冗余导致的特征表示能力差、建筑物边界不清晰及微小建筑物丢失问题。【方法】提出一种细节增强与跨尺度几何特征融合网络DCS-Net,由信息解耦与聚合模块(IRDM)、局部互相似性细节增强模块(LMSE)和引导小目标推理的跨尺度几何特征融合模块(CGFF)组成。IRDM模块通过分配权重将冗余特征分离并重构,从空间和通道2个维度抑制冗余,促进有效特征学习;LMSE模块通过动态选择窗格以及编-解码特征之间的局部互相似性指定像素聚类,提升建筑物边缘信息的准确性和完整性;CGFF模块计算原始图像与不同语义级特征图间的特征分块关系,补偿信息损失以提升微小建筑物的提取性能。【结果】本文的实验基于2个公开数据集:WHU航拍数据集和Massachusetts建筑物检测数据集。实验结果表明,与UNet、PSPNet、Deeplab V3+、MANet、MAPNet、DRNet、Build-Former、MBR-HRNet、SDSNet、HDNet、DFFNet、UANet等现有建筑物提取算法相比,DCS-Net在各项评价指标中得到了显著提升,验证了所提方法的有效性。在WHU数据集上的交并比、F1值和95%HD达到92.94%、96.35%和75.79,对比现有最佳算法分别提升了0.79%、0.44%和1.90;在Massachusetts数据集上的指标为77.13%、87.06%和205.26,分别提升了0.72%、0.43%和13.84。【结论】DCS-Net能更为准确完整地提取出遥感影像中的建筑物,并显著缓解微小建筑物丢失的问题。
[Objectives]To address issues in existing remote sensing building extraction models,including poor feature representation ability due to redundancy,unclear building boundaries,and the loss of small buildings,[Methods]we propose a detail enhancement and cross-scale geometric feature sharing network(DCS-Net).This network consists of an Information Decoupling and Aggregation Module(IRDM),a Local Mutual Similarity Detail Enhancement Module(LMSE),and a Cross-scale Geometric Feature Fusing Module(CGFF),designed to guide small target inference.The IRDM module separates and reconstructs redundant features by assigning weights,thereby suppressing redundancy in both spatial and channel dimensions and promoting effective feature learning.The LMSE module enhances the accuracy and completeness of building edge information by dynamically selecting windows and specifying pixel clustering based on local mutual similarity between encoderdecoder features.The CGFF module computes the feature block relationships between the original image and various semantic-level feature maps to compensate for information loss,thereby improving the extraction performance of small buildings.[Results]The experiments in this paper are based on two public datasets:the WHU aerial dataset and the Massachusetts building detection dataset.The experimental results demonstrate the following:(1)Compared with existing building extraction algorithms such as UNet,PSPNet,Deeplab V3+,MANet,MAPNet,DRNet,Build-Former,MBR-HRNet,SDSNet,HDNet,DFFNet,and UANet,DCS-Net has achieved significant improvements across various evaluation metrics,demonstrating the effectiveness of the proposed method.(2)On the WHU dataset,the Intersection over Union(IoU),F1 score,and 95%Hausdorff Distance(95%HD)reached 92.94%,96.35%,and 75.79%,respectively,outperforming the current best algorithm by 0.79%,0.44%,and 1.90%.(3)On the Massachusetts dataset,the metrics were 77.13%,87.06%,and 205.26,with improvements of 0.72%,0.43%,and 13.84%,respectively.[Conclusions]These results indicate that DCS-Net can more accurately and comprehensively extract buildings from remote sensing images,significantly alleviating the issue of small building loss.
作者
孟月波
苏世龙
黄欣羽
王恒
MENG Yuebo;SU Shilong;HUANG Xinyu;WANG Heng(Key Laboratory of Construction Robots for Higher Education in Shaanxi Province,Xi'an 710055,China;College of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China)
出处
《地球信息科学学报》
北大核心
2025年第4期930-945,共16页
Journal of Geo-information Science
基金
国家自然科学基金面上项目(52278125)。
关键词
遥感影像
建筑物提取
深度学习
信息解耦与聚合
细节增强
几何特征融合
像素语义聚类
微小目标补偿
remote sensing imagery
building extraction
deep learning
information decoupling and restructuring
detail enhancement
geometric feature fusing
pixel semantic clustering
small target compensation
