摘要
遥感影像建筑物准确、高效的自动提取方法有着广泛的用途。针对现有遥感影像建筑物提取方法难以兼顾不同大小的建筑物,导致小尺度建筑物不同程度上漏检及提取的建筑物轮廓边界模糊等问题,本文提出一种融合分散自适应注意力机制的多尺度遥感影像建筑物实例细化提取方法(MBRef-CNN)。首先采用融合分散自适应注意力机制的遥感影像多尺度特征提取网络(SA-FPN)学习多尺度建筑物的特征,然后利用区域候选网络(RPN)预测单个建筑物实例的目标框位置,最后使用边界细化网络(BndRN)迭代获取精确的建筑物掩膜。在WHU aerial imagery dataset数据集上,通过与现有主流方法进行对比试验表明,本文方法的建筑物掩膜提取精确度比其他表现优秀的主流分割算法更高,在多尺度的建筑物提取上表现出良好的综合性能,且在小尺度的建筑物提取上具有明显的精度优势。
The accurate and efficient automatic extraction of building footprints from remote sensing images has a wide range of applications.Since the buildings in remote sensing images have different types,scales,shapes and backgrounds,the existing methods,to varying degrees,suffer from the problems of missing small-scale buildings,blurred contour boundaries,and inability to distinguish individual building instances.Therefore,this paper proposed a multi-scale building instance refinement extraction convolutional neural network(MBRef-CNN)fusing with decentralized adaptive attention mechanism for remote sensing images.First,a feature pyramid network fused with split-attention and adaptive attention mechanism(SA-FPN)was used to learn multi-scale building features.Then,according to the multi-scale features,the region proposal network(RPN)was used to detect the location of individual building instances.Finally,the boundary refinement network(BndRN)was used to iteratively acquire the precise building masks.On WHU aerial imagery dataset,the comparison experiments were conducted with the existing popular segmentation methods.The results show that the accuracy of the proposed method in this paper is higher than the others.Moreover,the MBRef-CNN shows good comprehensive performance in multi-scale building extraction,and has obvious accuracy advantages in small-scale building extraction.
作者
江宝得
黄威
许少芬
巫勇
JIANG Baode;HANG Wei;XU Shaofen;WU Yong(School of Computer Science,China University of Geosciences,Wuhan 430074,China;National Engineering Research Center of Geographic Information System,China University of Geosciences,Wuhan 430074,China)
出处
《测绘学报》
EI
CSCD
北大核心
2023年第9期1504-1514,共11页
Acta Geodaetica et Cartographica Sinica
基金
国家自然科学基金(42171408)。
关键词
建筑物细化提取
分散注意力网络
自适应注意力机制
多尺度
遥感影像
深度学习
building refinement extraction
split-attention networks
adaptive attention mechanism
multi-scale
remote sensing images
deep learning
