张立亭,孔文学,罗亦泳,邓先金,夏文生

• 1:东华理工大学测绘工程学院

摘要(Abstract)：

针对现有的遥感影像建筑物提取方法存在着效率低下、精度不高等问题，该文利用轻量型分割网络LinkNet框架构建出新的建筑物提取全卷积网络。设计三层卷积模块替换LinkNet中的残差层作为新的编码块，有效减少网络参数，加快了网络训练速度；融合增强感受野模块聚合多尺度上下文信息，有利于图像特征细节的恢复，从而提高网络分割精度；综合上述两点构建出基于深度学习的高性能建筑物自动提取网络。在相应建筑物数据集上进行实验结果表明，本文构建的全卷积网络比已有的建筑物提取网络SE-Unet综合预测精度更高，取得82.80%的均交并比和95.99%的召回率，同时，在提取建筑物的完整度、边界分割精度等方面具有较好的效果。

关键词(KeyWords)： 遥感影像;建筑物提取;三层卷积模块;增强感受野模块;深度学习

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(41861058)

作者(Author): 张立亭,孔文学,罗亦泳,邓先金,夏文生

DOI: 10.16251/j.cnki.1009-2307.2022.09.015

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