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height estimation from single aerial imagery using contrastive learning based multi-scale refinement network

International Journal of Digital Earth 2023年第1期16卷 2322-2340页

作者： Wufan Zhao Hu Ding Jiaming Na Mengmeng Li Dirk Tiede Geomatics Section Department of Civil EngineeringFaculty of Engineering TechnologyKU LeuvenBelgium School of Geography South China Normal UniversityGuangzhouPeople’s Republic of China College of Civil Engineering Nanjing Forestry UniversityNanjingPeople’s Republic of China Academy of Digital China(Fujian) Fuzhou UniversityFuzhouPeople’s Republic of China Department of Geoinformatics(Z_GIS) Faculty of Digital and Analytical SciencesParis Lodron University of SalzburgSalzburgAustria

height map estimation from a single aerial image plays a crucial role in localization,mapping,and 3D object *** convolutional neural networks have been used to predict height information from single-view remote sensing images,but these methods rely on large volumes of training data and often overlook geometric features present in orthographic *** address these issues,this study proposes a gradient-based self-supervised learning network with momentum contrastive loss to extract geometric information from non-labeled images in the pretraining ***,novel local implicit constraint layers are used at multiple decoding stages in the proposed supervised network to refine high-resolution features in height *** structural-aware loss is also applied to improve the robustness of the network to positional shift and minor structural changes along the boundary *** evaluation on the ISPRS benchmark datasets shows that the proposed method outperforms other baseline networks,with minimum MAE and RMSE of 0.116 and 0.289 for the Vaihingen dataset and 0.077 and 0.481 for the Potsdam dataset,*** proposed method also shows around threefold data efficiency improvements on the Potsdam dataset and domain generalization on the Enschede *** results demonstrate the effectiveness of the proposed method in height map estimation from single-view remote sensing images.

关键词： height estimation aerial imagery digital surface models contrastive learning local implicit constrain

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Multi-task Learning of Semantic Segmentation and height estimation for Multi-modal Remote Sensing Images

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Journal of Geodesy and Geoinformation Science 2023年第4期6卷 27-39页

作者： Mengyu WANG Zhiyuan YAN Yingchao FENG Wenhui DIAO Xian SUN Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100190China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Network Information System Technology(NIST) Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100190China

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing ***,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal *** addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource *** this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height ***,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and height estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task *** results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.

关键词： multi-modal multi-task semantic segmentation height estimation convolutional neural network

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Target height and multipath attenuation joint estimation with complex scenarios for very high frequency radar

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Frontiers of Information Technology & Electronic Engineering 2022年第6期23卷 937-949页

作者： Sheng CHEN Yongbo ZHAO Yili HU Chenghu CAO Xiaojiao PANG National Laboratory ofRadar Signal Processing Xidian UniversityXi'an710071China

Low-angle estimation for very high frequency(VHF)radar is a difficult problem due to the multipath effect in the radar field,especially in complex scenarios where the reflection condition is *** deal with this problem,we propose an algorithm of target height and multipath attenuation joint *** amplitude of the surface reflection coefficient is estimated by the characteristic of the data itself,and it is assumed that there is no reflected signal when the amplitude is very *** phase of the surface reflection coefficient and the phase difference between the direct and reflected signals are searched as the same part,and this represents the multipath phase *** Cramer-Rao bound of the proposed algorithm is also ***,computer simulations and real data processing results show that the proposed algorithm has good estimation performance under complex scenarios and works well with only one snapshot.

关键词： Low-angle estimation Very high frequency(VHF)radar Complex scenarios Multipath effect height estimation

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