A5015385071- Advanced Image Fusion Techniques
- Remote Sensing in Agriculture
- Remote-Sensing Image Classification
- Image Enhancement Techniques
- Particle Detector Development and Performance
- Automated Road and Building Extraction
- Particle Accelerators and Free-Electron Lasers
- Atmospheric and Environmental Gas Dynamics
- Particle accelerators and beam dynamics
- Flood Risk Assessment and Management
- Photoacoustic and Ultrasonic Imaging
- Image and Signal Denoising Methods
- Advanced Image Processing Techniques
- Land Use and Ecosystem Services
China University of Mining and Technology
2020-2023
Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial different images to achieve continuous monitoring for Earth's surface, which is a feasible solution resolve trade-off between resolutions remote sensing images. In this paper, an object-based spatiotemporal model (OBSTFM) proposed produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology land cover without shape changes). Considering that might...
Spatiotemporal fusion technique provides a cost-efficient way to achieve dense time series observation. Among all categories of spatiotemporal methods, the weight function-based method attracted considerable attention. However, this kind selects similar pixels in regular window without considering distribution features, which will weaken its ability preserve structure information. Besides, carries out pixel-by-pixel computation, leads computational inefficiency. To solve aforementioned...
Spatiotemporal fusion (STF) is a cost-effective way to complement the spatiotemporal resolution of multi-source images, which has been employed in various applications requiring image sequences. In real-world applications, spectral accuracy, spatial accuracy and efficiency STF play critical role. Despite this, most methods focus on improving while challenges information loss low have received limited attention. Additionally, improvements are contradictory, existing cannot balance them well,...
ABSTRACTSpatiotemporal fusion (STF) is a cost-effective way to reconstruct time-series images. In recent years, deep learning-based (DL-based) STF methods have received substantial attention. However, two limitations of DL-based still remain: (1) existing require simultaneous learning both the multi-source images correction model and model, which complicates training task. The high complexity poses challenge for network accurately learn underlying mathematical principles STF, thereby...