Yujia Chen

Orcid: 0000-0002-2510-6333

Affiliations:
  • China University of Geosciences, School of Land Science and Technology, Beijing, China


According to our database1, Yujia Chen authored at least 10 papers between 2016 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Unsupervised Bayesian Subpixel Mapping Autoencoder Network for Hyperspectral Images.
IEEE Trans. Geosci. Remote. Sens., 2023

Subpixel Mapping for Remote Sensing Imagery Based on Spatial Adaptive Attraction Model and Conditional Random Fields.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

2022
Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

2021
Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

Unsupervised Bayesian Subpixel Mapping of Hyperspectral Imagery Based on Band-Weighted Discrete Spectral Mixture Model and Markov Random Field.
IEEE Geosci. Remote. Sens. Lett., 2021

2019
A Super-Resolution Convolutional-Neural-Network-Based Approach for Subpixel Mapping of Hyperspectral Images.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2019

Evaluation of Mine Exploitation Intensity Based on Topsis and BP Neural Network: a Case Study in Fujian Province, China.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

2016
Denoising of hyperspectral imagery using an intrinsic spectral representation model with spatial smoothness constraint.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016

Super-resolution reconstruction of hyperspectral imagery using an spectral unmixing based representational model.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016

A novel unsupervised classification approach for hyperspectral imagery based on spectral mixture model and MARKOV random field.
Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium, 2016


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