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20162024
most citedLand-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models

1k citations · 4.6k across the 34 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.CV2017★ 12 cited

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…

physics.ao-ph2017★ 137 cited

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…

physics.ao-ph2017

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…

q-bio.PE2017

A 33-year NPP monitoring study in southwest China by the fusion of multi-source remote sensing and station data

Xiaobin Guan, Huanfeng Shen, Wenxia Gan +4

Knowledge of regional net primary productivity (NPP) is important for the systematic understanding of the global carbon cycle. In this study, multi-source data were employed to con…

cs.CV2017★ 12 cited

Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"

Chengyue Zhang, Zhiwei Li, Qing Cheng +2

Remote sensing images often suffer from cloud cover. Cloud removal is required in many applications of remote sensing images. Multitemporal-based methods are popular and effective…

physics.ao-ph2017★ 417 cited

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…

cs.CV2017★ 490 cited

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…