Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review
Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both high spatial and temporal resolution. This bring...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823000587 |
| _version_ | 1811154723684745216 |
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| author | Guangsheng Chen Hailiang Lu Weitao Zou Linhui Li Mahmoud Emam Xuebin Chen Weipeng Jing Jian Wang Chao Li |
| author_facet | Guangsheng Chen Hailiang Lu Weitao Zou Linhui Li Mahmoud Emam Xuebin Chen Weipeng Jing Jian Wang Chao Li |
| author_sort | Guangsheng Chen |
| collection | DOAJ |
| description | Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both high spatial and temporal resolution. This brings difficulties to remote sensing research which requires high spatial and temporal resolution data. To solve the above constraints, the spatiotemporal fusion (STF) method was proposed and has received widespread attention. The main challenge for remote sensing STF is to reconstruct both phenological and land cover type changes. To overcome this challenge, many STF methods have been proposed based on different principles and strategies. Since there are STF methods have been proposed recently, there is a need for new review to reflect the current research status. Therefore, in this review, we summarize the existing studies, discusses their basic principle and limitations, collect some recent applications, and provide a comprehensive overview of current advances. Furthermore, to facilitate and promote the research in this community, we also collect publicly available resources and introduce the most used quantitative metrics. Finally, we take a conversation about some open problems and challenges that require attention in the future. |
| first_indexed | 2024-04-10T04:21:50Z |
| format | Article |
| id | doaj.art-864c865b41d545ecb85ff44096d654de |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-10T04:21:50Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-864c865b41d545ecb85ff44096d654de2023-03-11T04:19:24ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-03-01353259273Spatiotemporal fusion for spectral remote sensing: A statistical analysis and reviewGuangsheng Chen0Hailiang Lu1Weitao Zou2Linhui Li3Mahmoud Emam4Xuebin Chen5Weipeng Jing6Jian Wang7Chao Li8College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaFaculty of Artificial Intelligence, Menoufia University, Shebin El-Koom 32511, EgyptNorth China University of Science and Technology, Tangshan 063009, China; Hebei Key Laboratory of Data Science and Application, Tangshan 063009, China; Tangshan Key Laboratory of Data Science, Tangshan 063009, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, ChinaAerospace Information Research Institute, CAS, Beijing 100094, ChinaCollege of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China; Corresponding author at: Laboratory of Forestry Data Science and Cloud Computing of State Forestry Administration, Northeast Forestry University, Harbin, HLJ, China.Remote sensing images obtained by a variety of sensors have been widely used in different Earth observation tasks. However, owing to budget and sensor technology constraints, a single sensor cannot simultaneously provide observational images with both high spatial and temporal resolution. This brings difficulties to remote sensing research which requires high spatial and temporal resolution data. To solve the above constraints, the spatiotemporal fusion (STF) method was proposed and has received widespread attention. The main challenge for remote sensing STF is to reconstruct both phenological and land cover type changes. To overcome this challenge, many STF methods have been proposed based on different principles and strategies. Since there are STF methods have been proposed recently, there is a need for new review to reflect the current research status. Therefore, in this review, we summarize the existing studies, discusses their basic principle and limitations, collect some recent applications, and provide a comprehensive overview of current advances. Furthermore, to facilitate and promote the research in this community, we also collect publicly available resources and introduce the most used quantitative metrics. Finally, we take a conversation about some open problems and challenges that require attention in the future.http://www.sciencedirect.com/science/article/pii/S1319157823000587Remote sensing imageSpatiotemporal fusionWeightUnmixingMachine learningDeep learning |
| spellingShingle | Guangsheng Chen Hailiang Lu Weitao Zou Linhui Li Mahmoud Emam Xuebin Chen Weipeng Jing Jian Wang Chao Li Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review Journal of King Saud University: Computer and Information Sciences Remote sensing image Spatiotemporal fusion Weight Unmixing Machine learning Deep learning |
| title | Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review |
| title_full | Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review |
| title_fullStr | Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review |
| title_full_unstemmed | Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review |
| title_short | Spatiotemporal fusion for spectral remote sensing: A statistical analysis and review |
| title_sort | spatiotemporal fusion for spectral remote sensing a statistical analysis and review |
| topic | Remote sensing image Spatiotemporal fusion Weight Unmixing Machine learning Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823000587 |
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