Yinhe Liu
Orcid: 0000-0001-6227-2691
According to our database1,
Yinhe Liu
authored at least 15 papers
between 2021 and 2024.
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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
IEEE Trans. Geosci. Remote. Sens., 2024
Historical Product Driven Large-Scale High-Resolution Land Cover and Wetland Classification.
Proceedings of the IGARSS 2024, 2024
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
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021