525 citations · 2.9k across the 23 of their papers we have counts for
6 papers · 1 filter
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Qiangqiang Yuan, Yancong Wei, Xiangchao Meng +2
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employ…
Estimating regional ground-level PM2.5 directly from satellite top-of-atmosphere reflectance using deep learning
Huanfeng Shen, Tongwen Li, Qiangqiang Yuan +1
Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectan…
Learning a Dilated Residual Network for SAR Image Despeckling
Qiang Zhang, Qiangqiang Yuan, Jie Li +2
In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non…
The relationships between PM2.5 and meteorological factors in China: Seasonal and regional variations
Qianqian Yang, Qiangqiang Yuan, Tongwen Li +2
The interactions between PM2.5 and meteorological factors play a crucial role in air pollution analysis. However, previous studies that have researched the relationships between PM…
Estimating ground-level PM2.5 by fusing satellite and station observations: A geo-intelligent deep learning approach
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +2
Fusing satellite observations and station measurements to estimate ground-level PM2.5 is promising for monitoring PM2.5 pollution. A geo-intelligent approach, which incorporates ge…
Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network
Yancong Wei, Qiangqiang Yuan, Huanfeng Shen +1
In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the draw…