Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93521
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Gao, H | en_US |
dc.creator | Zhu, X | en_US |
dc.creator | Guan, Q | en_US |
dc.creator | Yang, X | en_US |
dc.creator | Yao, Y | en_US |
dc.creator | Zeng, W | en_US |
dc.creator | Peng, X | en_US |
dc.date.accessioned | 2022-07-08T01:02:55Z | - |
dc.date.available | 2022-07-08T01:02:55Z | - |
dc.identifier.issn | 0196-2892 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93521 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication Gao, H., Zhu, X., Guan, Q., Yang, X., Yao, Y., Zeng, W., & Peng, X. (2021). cuFSDAF: An enhanced flexible spatiotemporal data fusion algorithm parallelized using graphics processing units. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-16 is available at https://doi.org/10.1109/TGRS.2021.3080384 | en_US |
dc.subject | Compute unified device architecture (CUDA) | en_US |
dc.subject | Multisource satellite images | en_US |
dc.subject | Parallel computing | en_US |
dc.subject | Spatiotemporal data fusion | en_US |
dc.title | cuFSDAF : an enhanced flexible spatiotemporal data fusion algorithm parallelized using graphics processing units | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | en_US |
dc.identifier.epage | 16 | en_US |
dc.identifier.volume | 60 | en_US |
dc.identifier.doi | 10.1109/TGRS.2021.3080384 | en_US |
dcterms.abstract | Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of subdomains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements. First, the TPS interpolator is replaced by an accelerated inverse distance weighted (IDW) interpolator to reduce computational complexity. Second, the algorithm is parallelized based on the compute unified device architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). Third, an adaptive domain decomposition (ADD) method is proposed to improve the fusion accuracy at the edges of subdomains and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deep-learning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3-182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at https://github.com/HPSCIL/cuFSDAF. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on geoscience and remote sensing, 2022, v. 60, 4403016, p. 1-16 | en_US |
dcterms.isPartOf | IEEE transactions on geoscience and remote sensing | en_US |
dcterms.issued | 2022 | - |
dc.identifier.scopus | 2-s2.0-85107220598 | - |
dc.identifier.eissn | 1558-0644 | en_US |
dc.identifier.artn | 4403016 | en_US |
dc.description.validate | 202207 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | LSGI-0032, a1565 | - |
dc.identifier.SubFormID | 45448 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Key Research and Development Program of China; National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 53562120 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhu_cuFSFDAF.pdf | Pre-Published version | 2.69 MB | Adobe PDF | View/Open |
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