形状与位置先验信息引导的遥感影像建筑物域适应提取方法

赵绍轩; 周晓光; 侯东阳; 郭静; 刘华光

doi:10.11834/jrs.20255067

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形状与位置先验信息引导的遥感影像建筑物域适应提取方法
A domain-adaptive building extraction method from remote sensing imagery using shape and position priors
2025年页码：1-17
收稿：2009-01-06，

网络出版：2025-10-28，
DOI： 10.11834/jrs.20255067
稿件说明：

移动端阅览

赵绍轩，周晓光，侯东阳，郭静，刘华光.XXXX.形状与位置先验信息引导的遥感影像建筑物域适应提取方法.遥感学报，XX（XX）： 1-17 DOI： 10.11834/jrs.20255067.

Zhao Shaoxuan，Zhou Xiaoguang，Hou Dongyang，Guo Jing，Liu Huaguang. XXXX. A domain-adaptive building extraction method from remote sensing imagery using shape and position priors. National Remote Sensing Bulletin， XX（XX）：1-17 DOI： 10.11834/jrs.20255067.

摘要

深度学习为遥感影像建筑物提取提供了一种有效途径，但在训练数据（源域）与测试数据（目标域）分布不同的情况下，仅利用源域训练的模型直接应用在目标域时精度会显著下降。建筑物域适应提取方法可以克服域间数据分布差异，提升不同环境下建筑物信息的识别能力。针对建筑物域适应提取中目标域标签获取难度大、标注成本高，且当前方法未能充分利用建筑物不变特性提供跨域一致性约束的问题，本文提出了形状与位置先验信息引导的遥感影像建筑物域适应提取方法。首先，综合利用建筑物指数、Harris等方法自动提取目标域建筑物角点，并借助图像形态学方法提取源域标签建筑物边缘，作为两域的形状先验信息。然后，设计基于高斯转换的位置先验信息提取方法，将目标域OSM对象和源域标签对象分别转为双域的位置先验信息。最后，利用上述形状先验信息构建形状损失函数，同时在两域对建筑物目标提供训练约束，将双域位置先验信息作为独立附加通道叠合影像图层构成4通道输入，丰富目标域建筑物信息，设计基于对抗学习的建筑物域适应提取模型AU_AdaptNet。实验结果表明：本文方法的IoU指标比未经过域适应的基础模型提高了15%，比未添加先验信息引导的域适应模型提高了6%，且在目标域无OSM数据时，仅依靠形状先验信息引导也能提高建筑物域适应提取精度，在目标域OSM数据完整性较低的条件下，也可与使用高质量目标域标签的半监督域适应方法取得相近效果。

Abstract

Deep learning provides an effective approach for building extraction from remote sensing images. However

when the distribution of train data(source domain) and test data(target domain) is different

the accuracy of a model trained only in the source domain will significantly decrease when applied directly in the target domain. The domain adaptation method can overcome the differences in data distribution between domains

thereby improving the recognition ability of building information in different environments. Therefore

it improves the efficiency of urban planning

post disaster reconstruction

and land use management. The difficulty and cost of obtaining target domain labels in building domain adaptation extraction are high.The current methods fail to fully utilize the invariant properties of buildings to provide cross domain consistency constraints. This paper proposes a domain-adaptive building extraction method from remote sensing imagery guided by shape and position prior information. There are several main components to our approach. First

building corners in the target domain are automatically extracted through the comprehensive application of methods such as the Building Index and Harris operator

while the edges of labeled buildings in the source domain are obtained using image morphological processing. These extracted features serve as shape priors for both domains. Then

we design a Gaussian transformation-based method to convert the OSM objects in the target domain and the labeled objects in the source domain to the position prior information in the dual domain

respectively. Finally

a shape loss is constructed using the aforementioned shape prior information

while training constraints are provided for building in both domains. The dual domain position prior information is used as independent additional channels to superimpose image layers to form a 4-channel input

enriching the building information in the target domain. A building domain adaptation extraction model AU_adaptNet based on adversarial learning is designed.Experimental results show that the IoU metrics of our method can be improved by 15% over the results of the base generative model without domain adaptation

and by 6% over the results of the domain adaptation model without added prior information guidance. When there is no OSM data in the target domain

the domain adaptation accuracy can also be improved by relying only on shape prior information guidance. Under conditions of low OSM data integrity in the target domain

comparable results can be achieved with semi-supervised domain adaptation methods using high-quality target domain labels.We can draw the following conclusions (1) Our method extracts invariant features (e.g.

corners and edges) from source and target domain buildings as shape prior

while simultaneously incorporating crowdsourced data from the target domain as position prior. This leads to the development of a building extraction framework guided by both shape and position prior for domain adaptation. (2) Experimental results demonstrate that when OSM data in target domain exhibits low completeness

the proposed method yields comparable performance to semi-supervised domain adaptation approaches that utilize high-quality target domain labels.

关键词

Keywords

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