Xijie Cheng
Orcid: 0009-0001-2300-9695Affiliations:
- Zhengzhou University, Henan, China
According to our database1,
Xijie Cheng
authored at least 15 papers
between 2019 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Multi-Scale Attention Network for Building Extraction from High-Resolution Remote Sensing Images.
Sensors, February, 2024
Optimizing the Tobacco Leaf Quality Assessment System via Grey Relational and Circle Ill-Condition Index Analysis.
Proceedings of the 2024 7th International Conference on Information Management and Management Science, 2024
2023
MSCANet: multiscale context information aggregation network for Tibetan Plateau lake extraction from remote sensing images.
Int. J. Digit. Earth, December, 2023
KSTAGE: A knowledge-guided spatial-temporal attention graph learning network for crop yield prediction.
Inf. Sci., 2023
2022
Exploring Label Probability Sequence to Robustly Learn Deep Convolutional Neural Networks for Road Extraction With Noisy Datasets.
IEEE Trans. Geosci. Remote. Sens., 2022
An improved categorical cross entropy for remote sensing image classification based on noisy labels.
Expert Syst. Appl., 2022
Corrigendum to "Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction" [Int. J. Appl. Earth Observ. Geoinf. 104 (2021) 102544].
Int. J. Appl. Earth Obs. Geoinformation, 2022
Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images.
Int. J. Appl. Earth Obs. Geoinformation, 2022
2021
IEEE Trans. Geosci. Remote. Sens., 2021
Exploiting Hierarchical Features for Crop Yield Prediction Based on 3-D Convolutional Neural Networks and Multikernel Gaussian Process.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021
LR-RoadNet: A long-range context-aware neural network for road extraction via high-resolution remote sensing images.
IET Image Process., 2021
Crop yield prediction from multi-spectral, multi-temporal remotely sensed imagery using recurrent 3D convolutional neural networks.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Exploring multiple crowdsourced data to learn deep convolutional neural networks for road extraction.
Int. J. Appl. Earth Obs. Geoinformation, 2021
Improved Categorical Cross-Entropy Loss for Training Deep Neural Networks with Noisy Labels.
Proceedings of the Pattern Recognition and Computer Vision - 4th Chinese Conference, 2021
2019
Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset.
IEEE Access, 2019