An object-based spatiotemporal fusion model for remote sensing images
Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-01-01
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| Series: | European Journal of Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/22797254.2021.1879683 |
| _version_ | 1798033952479379456 |
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| author | Hua Zhang Yue Sun Wenzhong Shi Dizhou Guo Nanshan Zheng |
| author_facet | Hua Zhang Yue Sun Wenzhong Shi Dizhou Guo Nanshan Zheng |
| author_sort | Hua Zhang |
| collection | DOAJ |
| description | Spatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing images. In this paper, an object-based spatiotemporal fusion model (OBSTFM) is proposed to produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology changes and land cover changes without shape changes). Considering different changes that might occur in different regions, multi-resolution segmentation is first employed to produce segmented objects, and then a linear injection model is introduced to produce preliminary prediction. In addition, a new optimized strategy to select similar pixels is developed to obtain a more accurate prediction. The performance of proposed OBSTFM is validated using two remotely sensed dataset experiencing phenology changes in the heterogeneous area and land cover type changes, experimental results show that the proposed method is advantageous in such areas with non-shape changes, and has satisfactory robustness and reliability in blending large-scale abrupt land cover changes. Consequently, OBSTFM has great potential for monitoring highly dynamic landscapes. |
| first_indexed | 2024-04-11T20:36:41Z |
| format | Article |
| id | doaj.art-094838676c7042e6b231a9d8430ece98 |
| institution | Directory Open Access Journal |
| issn | 2279-7254 |
| language | English |
| last_indexed | 2024-04-11T20:36:41Z |
| publishDate | 2021-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| spelling | doaj.art-094838676c7042e6b231a9d8430ece982022-12-22T04:04:20ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542021-01-015418610110.1080/22797254.2021.18796831879683An object-based spatiotemporal fusion model for remote sensing imagesHua Zhang0Yue Sun1Wenzhong Shi2Dizhou Guo3Nanshan Zheng4China University of Mining and Technology XuzhouChina University of Mining and Technology XuzhouThe Hong Kong Polytechnic UniversityChina University of Mining and Technology XuzhouChina University of Mining and Technology XuzhouSpatiotemporal fusion technique can combine the advantages of temporal resolution and spatial resolution of different images to achieve continuous monitoring for the Earth’s surface, which is a feasible solution to resolve the trade-off between the temporal and spatial resolutions of remote sensing images. In this paper, an object-based spatiotemporal fusion model (OBSTFM) is proposed to produce spatiotemporally consistent data, especially in areas experiencing non-shape changes (including phenology changes and land cover changes without shape changes). Considering different changes that might occur in different regions, multi-resolution segmentation is first employed to produce segmented objects, and then a linear injection model is introduced to produce preliminary prediction. In addition, a new optimized strategy to select similar pixels is developed to obtain a more accurate prediction. The performance of proposed OBSTFM is validated using two remotely sensed dataset experiencing phenology changes in the heterogeneous area and land cover type changes, experimental results show that the proposed method is advantageous in such areas with non-shape changes, and has satisfactory robustness and reliability in blending large-scale abrupt land cover changes. Consequently, OBSTFM has great potential for monitoring highly dynamic landscapes.http://dx.doi.org/10.1080/22797254.2021.1879683spatiotemporal fusionsegmentationlinear injectionneighborhood information |
| spellingShingle | Hua Zhang Yue Sun Wenzhong Shi Dizhou Guo Nanshan Zheng An object-based spatiotemporal fusion model for remote sensing images European Journal of Remote Sensing spatiotemporal fusion segmentation linear injection neighborhood information |
| title | An object-based spatiotemporal fusion model for remote sensing images |
| title_full | An object-based spatiotemporal fusion model for remote sensing images |
| title_fullStr | An object-based spatiotemporal fusion model for remote sensing images |
| title_full_unstemmed | An object-based spatiotemporal fusion model for remote sensing images |
| title_short | An object-based spatiotemporal fusion model for remote sensing images |
| title_sort | object based spatiotemporal fusion model for remote sensing images |
| topic | spatiotemporal fusion segmentation linear injection neighborhood information |
| url | http://dx.doi.org/10.1080/22797254.2021.1879683 |
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