Yinhe Liu

Orcid: 0000-0001-6227-2691

According to our database1, Yinhe Liu authored at least 15 papers between 2021 and 2024.

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

Timeline

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Bibliography

2024
Cross-temporal high spatial resolution urban scene classification and change detection based on a class-weighted deep adaptation network.
Urban Inform., 2024

Occlusion-Aware Road Extraction Network for High-Resolution Remote Sensing Imagery.
IEEE Trans. Geosci. Remote. Sens., 2024

Historical Product Driven Large-Scale High-Resolution Land Cover and Wetland Classification.
Proceedings of the IGARSS 2024, 2024

Spliting Road Network to Road Segment Instances for Vector Road Mapping.
Proceedings of the IGARSS 2024, 2024

Large-Scale Tidal Wetland Classification Based on Label Augmentation and Error Correction.
Proceedings of the IGARSS 2024, 2024

Remote Sensing Image Land Cover Classification with Label Noise Based on Deep Reinforcement Learning.
Proceedings of the IGARSS 2024, 2024

MapChange: Enhancing Semantic Change Detection with Temporal-Invariant Historical Maps Based on Deep Triplet Network.
Proceedings of the IGARSS 2024, 2024

2023
Multi-temporal urban semantic understanding based on GF-2 remote sensing imagery: from tri-temporal datasets to multi-task mapping.
Int. J. Digit. Earth, December, 2023

Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China.
Int. J. Appl. Earth Obs. Geoinformation, April, 2023

A graph-based framework to integrate semantic object/land-use relationships for urban land-use mapping with case studies of Chinese cities.
Int. J. Geogr. Inf. Sci., 2023

High-Resolution Fine-Grained Wetland Mapping Based on Class-Balanced Deep Semantic Segmentation Networks.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

Seeing Beyond the Patch: Scale-Adaptive Semantic Segmentation of High-resolution Remote Sensing Imagery based on Reinforcement Learning.
Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022
Land-Use/Land-Cover Change Detection Based on Class-Prior Object-Oriented Conditional Random Field Framework for High Spatial Resolution Remote Sensing Imagery.
IEEE Trans. Geosci. Remote. Sens., 2022

Semantic Change Detection Based on a New Chinese Satellite Dataset and a Deep Conditional Random Field Framework.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
Weakly Supervised Semantic Change Detection via Label Refinement Framework.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021


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