1k citations · 4.6k across the 34 of their papers we have counts for
34 papers
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method
Jing Sun, Qiangqiang Yuan, Huanfeng Shen +2
The objective of image super-resolution is to reconstruct a high-resolution (HR) image with the prior knowledge from one or several low-resolution (LR) images. However, in the real…
A physics-constrained machine learning method for mapping gapless land surface temperature
Jun Ma, Huanfeng Shen, Menghui Jiang +5
More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-dr…
An attention mechanism based convolutional network for satellite precipitation downscaling over China
Yinghong Jing, Liupeng Lin, Xinghua Li +2
Precipitation is a key part of hydrological circulation and is a sensitive indicator of climate change. The Integrated Multi-satellitE Retrievals for the Global Precipitation Measu…
Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data
Jun Ma, Huanfeng Shen, Penghai Wu +3
Land surface temperature (LST) is a key parameter when monitoring land surface processes. However, cloud contamination and the tradeoff between the spatial and temporal resolutions…
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
Menghui Jiang, Huanfeng Shen, Jie Li +1
Image fusion technology is widely used to fuse the complementary information between multi-source remote sensing images. Inspired by the frontier of deep learning, this paper first…
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
Huanfeng Shen, Menghui Jiang, Jie Li +3
In the fields of image restoration and image fusion, model-driven methods and data-driven methods are the two representative frameworks. However, both approaches have their respect…
Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network
Liupeng Lin, Jie Li, Huanfeng Shen +3
The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages. To solves the problem of reduced resolution of…
A modified two-leaf light use efficiency model for improving the simulation of GPP using a radiation scalar
Xiaobin Guan, Jing M. Chen, Huanfeng Shen +1
A TL-LUE model modified with a radiation scalar (RTL-LUE) is developed in this paper. The same maximum LUE is used for both sunlit and shaded leaves, and the difference in LUE betw…
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion
Dong Chu, Huanfeng Shen, Xiaobin Guan +4
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise. Although numerous reconstruction methods…
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
Jiang He, Jie Li, Qiangqiang Yuan +2
Hyperspectral images are crucial for many research works. Spectral super-resolution (SSR) is a method used to obtain high spatial resolution (HR) hyperspectral images from HR multi…
Deep learning-based air temperature mapping by fusing remote sensing, station, simulation and socioeconomic data
Huanfeng Shen, Yun Jiang, Tongwen Li +3
Air temperature (Ta) is an essential climatological component that controls and influences various earth surface processes. In this study, we make the first attempt to employ deep…
An Urban Water Extraction Method Combining Deep Learning and Google Earth Engine
Yudie Wang, Zhiwei Li, Chao Zeng +2
Urban water is important for the urban ecosystem. Accurate and efficient detection of urban water with remote sensing data is of great significance for urban management and plannin…
Spatially Continuous and High-resolution Land Surface Temperature: A Review of Reconstruction and Spatiotemporal Fusion Techniques
Penghai Wu, Zhixiang Yin, Chao Zeng +6
Remotely sensed, spatially continuous and high spatiotemporal resolution (hereafter referred to as high resolution) land surface temperature (LST) is a key parameter for studying t…
Validation approaches for satellite-based PM2.5 estimation: Assessment and a new approach
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +1
Satellite-derived aerosol optical depth (AOD) has been increasingly employed for the estimation of ground-level PM2.5, which is often achieved by modeling the relationship between…
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
Zhiwei Li, Huanfeng Shen, Qing Cheng +3
Cloud detection is an important preprocessing step for the precise application of optical satellite imagery. In this paper, we propose a deep learning based cloud detection method…
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
Qiang Zhang, Qiangqiang Yuan, Jie Li +3
The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs appl…
Oblique Stripe Removal in Remote Sensing Images via Oriented Variation
Xinxin Liu, Xiliang Lu, Huanfeng Shen +2
Destriping is a classical problem in remote sensing image processing. Although considerable effort has been made to remove stripes, few of the existing methods can eliminate stripe…
Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces
Yuan Wang, Qiangqiang Yuan, Tongwen Li +3
In this study, we evaluated the performance of the MODIS C6.1 AOD products and compared them with the C6 products over regions in China with multifarious underlying surfaces during…
Spatial-Spectral Fusion by Combining Deep Learning and Variation Model
Huanfeng Shen, Menghui Jiang, Jie Li +3
In the field of spatial-spectral fusion, the model-based method and the deep learning (DL)-based method are state-of-the-art. This paper presents a fusion method that incorporates…
Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5
Tongwen Li, Huanfeng Shen, Qiangqiang Yuan +1
The integration of satellite-derived aerosol optical depth (AOD) and station-measured PM2.5 provides a promising approach for obtaining spatial PM2.5 data. Several spatiotemporal m…
The relationships between PM2.5 and AOD in China: About and behind spatiotemporal variations
Qianqian Yang, Qiangqiang Yuan, Linwei Yue +3
Satellite aerosol products have been widely used to retrieve ground PM2.5 concentration because of its wide coverage and continuously spatial distribution. While more and more stud…
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models
Xin-Yi Tong, Gui-Song Xia, Qikai Lu +4
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the i…
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Qiangqiang Yuan, Qiang Zhang, Jie Li +2
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications. In this paper, a novel de…
Real-time and Seamless Monitoring of Ground-level PM2.5 Using Satellite Remote Sensing
Tongwen Li, Chengyue Zhang, Huanfeng Shen +2
Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is stil…