A CNN-Based Sentinel-2 Image Super-Resolution Method Using Multiobjective Training

Author:

Vasilescu Vlad1ORCID,Datcu Mihai2ORCID,Faur Daniela3ORCID

Affiliation:

1. Speech and Dialogue Laboratory and the Center for Spatial Information, University Politehnica of Bucharest, Bucharest, Romania

2. Remote Sensing Technology Institute, EO Data Science, German Aerospace Center (DLR), Weßling, Germany

3. Center for Spatial Information, University Politehnica of Bucharest, Bucharest, Romania

Funder

CENTURION - Copernicus Datacube and AI Services for Society, Industry and New Market Generation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Local-Global Context-Aware Generative Dual-Region Adversarial Networks for Remote Sensing Scene Image Super-Resolution;IEEE Transactions on Geoscience and Remote Sensing;2024

2. RGTGAN: Reference-Based Gradient-Assisted Texture-Enhancement GAN for Remote Sensing Super-Resolution;IEEE Transactions on Geoscience and Remote Sensing;2024

3. A Latent Analysis of A Super-Resolved Sentinel-2 Data Cube For Green Urban Infrastructure Health Monitoring;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. Sentinel-2 60-m Band Super-Resolution Using Hybrid CNN-GPR Model;IEEE Geoscience and Remote Sensing Letters;2023

5. Unsupervised Sentinel-2 Image Fusion Using a Deep Unrolling Method;IEEE Geoscience and Remote Sensing Letters;2023

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