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Qunying Huang

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As seen in: Wiley Online Library, MDPI, Taylor & Francis Online, Springer Professional
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Articles

Deep learning for single-image super-resolution in remote sensing: a review

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By Songxi Yang, Hamed Ebrahimian, Zhou Zhang, Qunying Huang
| Taylor & Francis Online
ABSTRACT Remote sensing image super-resolution has become a critical task to enhance spatial details for downstream applications such as land cover mapping, environmental monitoring, and precision agriculture. However, the unique characteristics of remote sensing data – including limited training samples, complex sensor degradations, and spectral diversity – pose significant challenges to conventional super-resolution pipelines.
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GeoAwareDiffSR: geospatial-aware remote sensing super resolution with diffusion priors and multi-modal constraints for crop mapping

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By Qunying Huang, Songxi Yang, Tang Sui
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. Abstract Super resolution offers a way to harness medium- even low-resolution but historically valuable remote sensing image archives.
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Temporal-Spatial Evolution and Driving Mechanism for an Ecosystem Health Service Based on the GD-MGWR-XGBOOT-SEM Model: A Case Study in Guangxi Region

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By Zhenfeng Wei, dong chen, Qunying Huang, Qifeng Chen
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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