Xiaozheng Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Xiaozheng Li has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Jianfeng Liu
Software College, Henan University of Science and Technology, No. 273, Kaiyuan Avenue, Luolong District, Luoyang Henan 471023, P. R. China
1 shared publication - Lifan Sun
Henan University of Science and Technology
1 shared publication - Ruijuan Zheng
School of Information Engineering, Henan University of Science and Technology, Luoyang, China
1 shared publication - Xiaoli Song
School of Software, Henan University of Science and Technology, Luoyang 471000, China
1 shared publication - Yanna Si
School of Information Engineering, Henan University of Science and Technology, Luoyang 471000, China
1 shared publication - Yibo Du
School of Software, Henan University of Science and Technology, Luoyang 471000, China
1 shared publication
Latest Publications
- Open Access
- 19 May 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (10) , 1632
GRCD-Net: Guided Global–Local Relational Learning for Few-Shot Fine-Grained and Remote Sensing Scene Classification
Remote sensing scene classification (RSSC) faces severe challenges from data scarcity and complex background clutter. To overcome these limitations, this paper draws inspiration from few-shot fine-grained image classification (FSFGIC) to filter noise and capture subtle details. However, existing methods often process global context and local features separately, which limits their ability to suppress background noise in complex scenes. Consequently, the Guided Relational Cross-Attention Dual-branch Network (GRCD-Net) is proposed. Its core Guided Relational Cross-Attention (GRC) block leverages global semantics to filter local background noise prior to bidirectional feature interaction. Additionally, Iterative Global Relation (IGR) and Patch-level Dual-Metric (PDM) modules are integrated to robustly refine global relations and capture local similarities. Extensive experiments demonstrate that GRCD-Net consistently outperforms baselines by 2–4% on standard FSFGIC benchmarks. Notably, on the challenging NWPU-RESISC45 RSSC dataset, it achieves an 81.39% one-shot accuracy and exceeds current state-of-the-art methods by 7.55%, validating its efficacy for complex Earth observation.
- 19 May 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (10) , 1632
Open AccessGRCD-Net: Guided Global–Local Relational Learning for Few-Shot Fine-Grained and Remote Sensing Scene Classification
Remote sensing scene classification (RSSC) faces severe challenges from data scarcity and complex background clutter. To overcome these limitations, this paper draws inspiration from few-shot fine-grained image classification (FSFGIC) to filter noise and capture subtle details. However, existing methods often process global context and local features separately, which limits their ability to suppress background noise in complex scenes. Consequently, the Guided Relational Cross-Attention Dual-branch Network (GRCD-Net) is proposed. Its core Guided Relational Cross-Attention (GRC) block leverages global semantics to filter local background noise prior to bidirectional feature interaction. Additionally, Iterative Global Relation (IGR) and Patch-level Dual-Metric (PDM) modules are integrated to robustly refine global relations and capture local similarities. Extensive experiments demonstrate that GRCD-Net consistently outperforms baselines by 2–4% on standard FSFGIC benchmarks. Notably, on the challenging NWPU-RESISC45 RSSC dataset, it achieves an 81.39% one-shot accuracy and exceeds current state-of-the-art methods by 7.55%, validating its efficacy for complex Earth observation.