Yong Li
7 Papers
Yong Li is an academic researcher. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 2, co-authored 2 publications.
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Papers
Atmospheric Light Estimation Based Remote Sensing Image Dehazing
TL;DR: Zhang et al. as mentioned in this paper used a differentiable function to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images, and then the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model.
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Remote Sensing Image Defogging Networks Based on Dual Self-Attention Boost Residual Octave Convolution
TL;DR: In this article, a dual self-attention boost residual octave convolution (DOC) is used to decompose a source image into high and low-frequency components, and the feature maps of each network layer are passed to the corresponding network layer in the decoding stage.
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Remote Sensing of Chlorophyll-a in Xinkai Lake Using Machine Learning and GF-6 WFV Images
TL;DR: Based on GF-6 WFV images and field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning as mentioned in this paper .
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Sentinel-3 OLCI observations of Chinese lake turbidity using machine learning algorithms
Yong Li,Sijia Li,Kaishan Song,Ge Liu,Zhidan Wen,Chong Fang,Yingxin Shang,Lili Lyu,Lele Zhang +8 more
TL;DR: Wang et al. as mentioned in this paper developed suitable turbidity algorithms for mapping the dynamics of lake turbidity using OLCI imagery, which can generate turbidity data for large-scale monitoring and decision-making related to environmental protection.
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Remote estimation of phycocyanin concentration in inland waters based on optical classification.
Lili Lyu,Kaishan Song,Zhidan Wen,Ge Liu,Chong Fang,Yingxin Shang,Sijia Li,Hui-Hui Tao,Xiang Wang,Yong Li,Xiang Wang +10 more
TL;DR: This study develops a remote sensing framework to estimate phycocyanin concentration in inland waters, improving accuracy by 80.4% using optical classification and three candidate algorithms, suitable for various water types and turbidity levels.
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