no code implementations • 2 Aug 2023 • Yiming Zhou, Yuexing Peng, Wei Li, Junchuan Yu, Daqing Ge, Wei Xiang
To extract accurate semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from boundaries of landslides through HPCL-Net and fuses heterogeneous infromation in the semantic space from high-resolution remote sensing images and digital elevation model data.
no code implementations • 24 Feb 2023 • Zili Lu, Yuexing Peng, Wei Li, Junchuan Yu, Daqing Ge, Wei Xiang
An object-level contrastive learning (OCL) strategy is employed in the object classification sub-network featuring a siamese network to realize the global features extraction, and a sub-object-level contrastive learning (SOCL) paradigm is designed in the semantic segmentation sub-network to efficiently extract salient features from boundaries of landslides.
no code implementations • 10 Jun 2022 • Han Meng, Yuexing Peng, Wei Xiang, Xu Pang, Wenbo Wang
In this paper, a two-stream semantic feature fusion model, termed Multi-faceted Graph Attention Network (MF-GAT), is proposed to greatly improve the accuracy in the low SNR region of the heterogeneous radar network.
no code implementations • 15 Apr 2022 • Han Meng, Yuexing Peng, Wenbo Wang, Peng Cheng, Yonghui Li, Wei Xiang
This paper proposes a knowledge-and-data-driven graph neural network-based collaboration learning model for reliable aircraft recognition in a heterogeneous radar network.
no code implementations • 24 Feb 2022 • Zeyang Wu, Wenbo Wang, Yuexing Peng
A low-altitude UAV detection method based on deep contrastive learning is proposed to address the above problems: Concretely, a low-altitude UAV radar echo model under low-altitude clutter interference is first established.
no code implementations • 18 Jan 2022 • Ying Wang, Yuexing Peng, Xinran Liu, Wei Li, George C. Alexandropoulos, Junchuan Yu, Daqing Ge, Wei Xiang
Extracting roads from high-resolution remote sensing images (HRSIs) is vital in a wide variety of applications, such as autonomous driving, path planning, and road navigation.