基于深度学习的卫星影像立体匹配算法

Satellite images stereo matching algorithm based on deep learning

卫星影像立体匹配是大规模地球表面重建的重要步骤之一,现有研究相对较少,且存在匹配效果不佳、模型泛化能力较差等问题。本文提出了一种基于深度学习的卫星影像立体匹配算法,算法流程包括数据集构建、立体匹配网络搭建、多级迁移学习及后处理。首先将裁剪得到的核线影像对输入改进的注意力成本网络,完成特征提取、注意力成本构建、视差估计与视差优化;然后经多级迁移学习训练的网络,可适应不同数据源,预测得到视差图;最后对视差图进行自适应后处理,以消除错误匹配。使用吉林一号高分02、高分04系列卫星影像进行试验,获取的视差图精度优于1像素,表明使用本文算法可获取准确、清晰的视差估计,决定了后续高质量数字表面模型结果的生成。

Satellite images stereo matching is one of the important steps for large-scale earth surface reconstruction,there are relatively few existing studies,and there are problems such as poor matching effect and poor model generalization ability.A deep learning-based satellite image stereo matching algorithm is proposed to perform stereo matching,including dataset construction,building stereo matching network,multi-level transfer learning and post-processing.Dataset construction contains disparity offset and and cropping.The cropped patches are then fed into attention volume network,which includes deep feature extraction,attention volume construction,disparity estimation.The network is trained by multi-level transfer learning,adapts to different data sources,and predicts the disparity maps.The false matches are filtered out by post-processing.The experiments were carried out with Jilin1-GF02 and Jilin1-GF04 images.The accuracy of the disparity maps obtained from the experimental results is better than one pixel.It shows that the algorithm proposed in this paper can obtain accurate and efficient results,which determines the generation of subsequent high-quality digital surface model.