Wei Zhang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Wei Zhang has a total of 12 co-authors. Here are the top co-authors by number of shared publications:
- Boxiang Xie
Aulin College, Northeast Forestry University, Harbin 150042, China
2 shared publications - Lingfeng Lin
College of Life Science, Northeast Forestry University, Harbin, China
2 shared publications - Yao Zhang
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
2 shared publications - Haobo Xu
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
1 shared publication - Honggang Li
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China
1 shared publication - Jiajin Wu
College of Control and Information Engineering, Northeast Forestry University, Harbin 150040, China
1 shared publication
Latest Publications
- Open Access
- 9 July 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (14) , 2299
PanDiM: A Diffusion Mamba Network for High-Fidelity Pansharpening
Pansharpening plays an important role in remote sensing image processing. Its purpose is to fuse a high-spatial-resolution panchromatic (PAN) image and a low-spatial-resolution multispectral (LRMS) image, thereby reconstructing a high-resolution multispectral (HRMS) image with both high spatial clarity and high spectral fidelity. In recent years, diffusion models have shown great potential in image generation. However, existing diffusion-based pansharpening methods usually adopt a fixed denoising strategy, making it difficult to adapt to the stage-wise changes in the denoising process and complex degradation distributions. Based on this, we propose PanDiM, an efficient generative framework for pansharpening. Specifically, we reformulate pansharpening as a high-frequency residual restoration process constrained by multimodal conditions. To improve the response accuracy of the model in complex regions, we design a Degradation-Posterior Guidance Module (DPGM), which extracts dual-scale physical detail priors from the PAN image, explicitly infers the degradation posterior, and converts it into dynamic control variables to adaptively regulate the state evolution of Mamba. In addition, we propose a time-aware mechanism, which allows temporal information to directly intervene in posterior estimation and state-space modeling, so as to accurately match the modeling requirements of different denoising stages. Considering the characteristics of residual reconstruction, we further propose a frequency-decoupled loss (FDL), which separates low- and high-frequency components in the frequency domain and applies targeted constraints. This significantly enhances the model’s ability to represent textures and achieves more robust spectral fidelity. Extensive experiments on three benchmark datasets, including WorldView-3, GaoFen-2, and QuickBird, show that PanDiM significantly outperforms existing mainstream methods in both reduced-resolution and full-resolution evaluations, providing a new solution for high-fidelity pansharpening in complex scenarios.
- 9 July 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (14) , 2299
Open AccessPanDiM: A Diffusion Mamba Network for High-Fidelity Pansharpening
Pansharpening plays an important role in remote sensing image processing. Its purpose is to fuse a high-spatial-resolution panchromatic (PAN) image and a low-spatial-resolution multispectral (LRMS) image, thereby reconstructing a high-resolution multispectral (HRMS) image with both high spatial clarity and high spectral fidelity. In recent years, diffusion models have shown great potential in image generation. However, existing diffusion-based pansharpening methods usually adopt a fixed denoising strategy, making it difficult to adapt to the stage-wise changes in the denoising process and complex degradation distributions. Based on this, we propose PanDiM, an efficient generative framework for pansharpening. Specifically, we reformulate pansharpening as a high-frequency residual restoration process constrained by multimodal conditions. To improve the response accuracy of the model in complex regions, we design a Degradation-Posterior Guidance Module (DPGM), which extracts dual-scale physical detail priors from the PAN image, explicitly infers the degradation posterior, and converts it into dynamic control variables to adaptively regulate the state evolution of Mamba. In addition, we propose a time-aware mechanism, which allows temporal information to directly intervene in posterior estimation and state-space modeling, so as to accurately match the modeling requirements of different denoising stages. Considering the characteristics of residual reconstruction, we further propose a frequency-decoupled loss (FDL), which separates low- and high-frequency components in the frequency domain and applies targeted constraints. This significantly enhances the model’s ability to represent textures and achieves more robust spectral fidelity. Extensive experiments on three benchmark datasets, including WorldView-3, GaoFen-2, and QuickBird, show that PanDiM significantly outperforms existing mainstream methods in both reduced-resolution and full-resolution evaluations, providing a new solution for high-fidelity pansharpening in complex scenarios.
- Open Access
- 1 June 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (11) , 1763
MPI-DETR: Multi-Grain Prompt and Intensity-Guided Transformer for Small-Object Detection in UAV Imagery
Small object detection in unmanned aerial vehicle (UAV) remote sensing images remains challenging due to large-scale variations, dense object distributions, and complex background interference. Although Transformer-based detectors have improved global context modeling for remote sensing object detection, many existing designs still rely on spatial geometric relationships and conventional cross-level fusion, which may limit the aggregation of spatially scattered small-object features and introduce background interference. To address these issues, this paper proposes a Multi-granularity Prompt and Intensity Guidance Detection Transformer (MPI-DETR), an efficient end-to-end Transformer-based detector for small-object detection in UAV remote sensing images. MPI-DETR consists of three key components: a Dual-Stream Ranked Self-Attention (DRSA) module for intensity-ordered global feature aggregation, a Bilateral Tanh Gating and Cosine Attention Feature Alignment Module (BTC-FAM) for noise-resistant cross-level alignment, and a Prompt-driven Multi-granularity Fusion (PMGF) module for enhancing weak small-object details. Experiments on AI-TOD, DIOR, and NWPU VHR-10 demonstrate that MPI-DETR achieves scores of , , and , respectively. Compared with the RT-DETR-R18 baseline, MPI-DETR improves by 3.0, 1.0, and 3.9 percentage points on the three datasets, respectively, and increases by 2.9, 2.9, and 11.6 percentage points. It also surpasses the strongest compared models by 1.7, 1.0, and 1.3 percentage points in on AI-TOD, DIOR, and NWPU VHR-10, respectively. These results indicate that MPI-DETR provides a robust and efficient solution for small-object perception in complex UAV remote sensing applications, especially in scenes with dense targets, background interference, and limited computational resources.- 1 June 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (11) , 1763
Open AccessMPI-DETR: Multi-Grain Prompt and Intensity-Guided Transformer for Small-Object Detection in UAV Imagery
Small object detection in unmanned aerial vehicle (UAV) remote sensing images remains challenging due to large-scale variations, dense object distributions, and complex background interference. Although Transformer-based detectors have improved global context modeling for remote sensing object detection, many existing designs still rely on spatial geometric relationships and conventional cross-level fusion, which may limit the aggregation of spatially scattered small-object features and introduce background interference. To address these issues, this paper proposes a Multi-granularity Prompt and Intensity Guidance Detection Transformer (MPI-DETR), an efficient end-to-end Transformer-based detector for small-object detection in UAV remote sensing images. MPI-DETR consists of three key components: a Dual-Stream Ranked Self-Attention (DRSA) module for intensity-ordered global feature aggregation, a Bilateral Tanh Gating and Cosine Attention Feature Alignment Module (BTC-FAM) for noise-resistant cross-level alignment, and a Prompt-driven Multi-granularity Fusion (PMGF) module for enhancing weak small-object details. Experiments on AI-TOD, DIOR, and NWPU VHR-10 demonstrate that MPI-DETR achieves scores of , , and , respectively. Compared with the RT-DETR-R18 baseline, MPI-DETR improves by 3.0, 1.0, and 3.9 percentage points on the three datasets, respectively, and increases by 2.9, 2.9, and 11.6 percentage points. It also surpasses the strongest compared models by 1.7, 1.0, and 1.3 percentage points in on AI-TOD, DIOR, and NWPU VHR-10, respectively. These results indicate that MPI-DETR provides a robust and efficient solution for small-object perception in complex UAV remote sensing applications, especially in scenes with dense targets, background interference, and limited computational resources.