【摘要】 目的 近年来，深度学习用于提取影像中的建筑物信息已经成为遥感领域研究热点之一，为了准确高效地提取遥感影像中的建筑物信息。方法 使用ResNet替换U-Net的骨干网络，并以此为基础进行改进，融合自校正卷积(SCCnov)和高效通道注意力(ECA),构建了一种新的建筑物提取网络模型。使用马萨诸塞州建筑物数据集设计消融实验，对所构建的网络模型的提取精度和实际效果进行对比分析，并使用WHU数据集验证网络的普适性，同时使用安徽某矿区的无人机影像数据集设计了迁移性实验，验证所构建网络的迁移能力。结果 所构建的网络在mIoU、mPrecision和mRecall 3个指标上分别达到了82.89%、92.26%和88.32%,较改进前分别提升了1.70%、1.08%和1.19%;另外，在迁移性实验中，网络在mIoU、mPrecision和mRecall 3个指标上分别达到88.66%、94.37%和93.19%。结论 本文所提出的SCEC-Unet在建筑物提取中具有良好的效果，且在独立小建筑物，异形建筑以及边缘建筑物的提取中表现较好，同时该网络具有良好的迁移能力，可用于矿区建筑物提取任务的迁移学习。更多还原

【Abstract】 Objective In recent years, the use of deep learning to extract building information from images has become one of the research hotspots in the field of remote sensing.In order to accurately and efficiently extract the building information in the remote sensing imagery.Methods The backbone network of U-Net was replaced by ResNet, and a new building extraction network model was constructed by fusing Self-Corrected Convolution(SCCnov) and Efficient Channel Attention(ECA).The Massachusetts building dataset was used to design an ablation experiment, the extraction accuracy and practical effect of the constructed network model were compared and analyzed, and the universality of the network was verified by using the WHU dataset, and the migration experiment was designedby using the UAV image dataset of a mining area in Anhui Province to verify the migration ability of the constructed network.Results The constructed network reached 82.89%,92.26% and 88.32% on the three indexes of mIoU,mPrecision and mRecall respectively, which were 1.70%,1.08% and 1.19% higher than those before the improvement.In addition, in the migration experiment, the network reached 88.66%,94.37% and 93.19% on the three indicators of mIoU,mPrecision and mRecall, respectively.Conclusion The SCEC-Unet proposed in theresearch has good results in building extraction and performs well in the extraction of independent small buildings, special-shaped buildings and edge buildings, and the network has good migration ability, which can be used for transfer learning of building extraction tasks in mining areas.更多还原