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- Publisher Website: 10.1016/j.rse.2023.113616
- Scopus: eid_2-s2.0-85159552844
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Article: ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications
| Title | ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications |
|---|---|
| Authors | |
| Keywords | Computing efficiency Missing data Seamless data cube Spatiotemporal fusion Time series |
| Issue Date | 20-May-2023 |
| Publisher | Elsevier |
| Citation | Remote Sensing of Environment, 2023, v. 294 How to Cite? |
| Abstract | Dense time-series high-resolution satellite images are extremely valuable for long-term monitoring of land dynamics. Spatiotemporal fusion (STF) techniques have been developed to integrate multi-resolution satellite images to produce data with high spatial resolution and temporal frequency. Due to the large volume and diversity of higher resolution global Earth Observation (EO) data, large-scale data processing methods need to be computationally efficient, free of parameter fine-tuning, and adaptive to various data structures without process customization. These requirements are especially critical for the production of global Seamless Data Cube (SDC). Considering the limitations of existing STF methods, we propose a ROBust OpTimization-based (ROBOT) fusion model that exploits time-series information to obtain more accurate predictions. ROBOT maintains stable under varied data conditions by adopting a temporal-coherence regularization term. And being free of parameter tuning, ROBOT can be applied to arbitrary spatiotemporally distributed data without repetitive process customization. Its performance was compared with eight representative STF methods. Results show that ROBOT outperforms existing STF methods in most cases and is computationally efficient, about 4000-fold faster than ESTARFM and 600-fold faster than FSDAF. The proposed method has demonstrated its potential for global-scale SDC generation to support subsequent remote sensing applications. |
| Persistent Identifier | http://hdl.handle.net/10722/331955 |
| ISSN | 2023 Impact Factor: 11.1 2023 SCImago Journal Rankings: 4.310 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chen, Shuang | - |
| dc.contributor.author | Wang, Jie | - |
| dc.contributor.author | Gong, Peng | - |
| dc.date.accessioned | 2023-09-28T04:59:51Z | - |
| dc.date.available | 2023-09-28T04:59:51Z | - |
| dc.date.issued | 2023-05-20 | - |
| dc.identifier.citation | Remote Sensing of Environment, 2023, v. 294 | - |
| dc.identifier.issn | 0034-4257 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/331955 | - |
| dc.description.abstract | <p><span>Dense time-series high-resolution satellite images are extremely valuable for long-term monitoring of land dynamics. Spatiotemporal fusion (STF) techniques have been developed to integrate multi-resolution satellite images to produce data with </span><a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/high-spatial-resolution" title="Learn more about high spatial resolution from ScienceDirect's AI-generated Topic Pages">high spatial resolution</a><span> and temporal frequency. Due to the large volume and diversity of higher resolution global Earth Observation (EO) data, large-scale data processing methods need to be computationally efficient, free of parameter fine-tuning, and adaptive to various data structures without process customization. These requirements are especially critical for the production of global Seamless Data Cube (SDC). Considering the limitations of existing STF methods, we propose a ROBust OpTimization-based (ROBOT) fusion model that exploits time-series information to obtain more accurate predictions. ROBOT maintains stable under varied data conditions by adopting a temporal-coherence regularization term. And being free of parameter tuning, ROBOT can be applied to arbitrary spatiotemporally distributed data without repetitive process customization. Its performance was compared with eight representative STF methods. Results show that ROBOT outperforms existing STF methods in most cases and is computationally efficient, about 4000-fold faster than ESTARFM and 600-fold faster than <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/multisensor-fusion" title="Learn more about FSDAF from ScienceDirect's AI-generated Topic Pages">FSDAF</a>. The proposed method has demonstrated its potential for global-scale SDC generation to support subsequent <a href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/remote-sensing-application" title="Learn more about remote sensing applications from ScienceDirect's AI-generated Topic Pages">remote sensing applications</a>.</span><br></p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Remote Sensing of Environment | - |
| dc.subject | Computing efficiency | - |
| dc.subject | Missing data | - |
| dc.subject | Seamless data cube | - |
| dc.subject | Spatiotemporal fusion | - |
| dc.subject | Time series | - |
| dc.title | ROBOT: A spatiotemporal fusion model toward seamless data cube for global remote sensing applications | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.rse.2023.113616 | - |
| dc.identifier.scopus | eid_2-s2.0-85159552844 | - |
| dc.identifier.volume | 294 | - |
| dc.identifier.isi | WOS:001009494700001 | - |
| dc.identifier.issnl | 0034-4257 | - |