A5013527857- Geoscience and Mining Technology
- Advanced Image Processing Techniques
- Advanced Image Fusion Techniques
- Remote Sensing in Agriculture
- Image and Signal Denoising Methods
- Image Enhancement Techniques
- Cryospheric studies and observations
- Species Distribution and Climate Change
- Flood Risk Assessment and Management
- Remote Sensing and LiDAR Applications
- Hydrology and Watershed Management Studies
Henan Polytechnic University
2022-2025
The Normalized Difference Vegetation Index (NDVI) is an essential metric in vegetation monitoring for remote sensing applications. While there are numerous long-term low-resolution NDVI datasets available remains unmet need high-resolution reconstructions over extended time frames. Existing research has not comprehensively assessed the efficacy of various temporal fusion techniques reconstruction at large regional scales. Traditional Spatiotemporal Image Fusion (TSTIF) methods often suffer...
Traditional remote sensing spatio-temporal data fusion algorithms generally use up-sampled low-resolution images (MODIS) to be fused with high-resolution (Landsat), this makes both less spatially consistent and many hybrid image elements in images, so uncertainty errors propagate into the results. To address issue, we propose a framework for combining deep learning-based super-resolution techniques traditional methods. By reconstructing using reconstruction techniques, obtain more spatial...
Global warming has led to an increase in interest river hydrology. Water surface slope is a basic parameter of morphology and critical understanding riverine ecosystems. The Yarlung Zangbo River important international river. Scientific its hydrology been severely limited by harsh topography the sparse distribution measurement stations. This paper describes first attempt use ICESat-2 satellite altimetry data calculate water introduces new technique for studying level different stretches were...
Flexible spatio-temporal data fusion (FSDAF) is usually used to fuse high spatial resolution images with ordinary up-sampling methods processed low images. However, lead inconsistency between and images, as well the presence of many mixed pixels in which reduces accuracy. In this study, a novel method by combining deep learning downscaling FSDAF proposed. This (RCAN-FSDAF) firstly downscales using residual channel attention network (RCAN), then fuses downscaled finally obtain data. The...