An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images
The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Lan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-08-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/9/1388 |
| _version_ | 1819294818948349952 |
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| author | Jianhang Ma Wenjuan Zhang Andrea Marinoni Lianru Gao Bing Zhang |
| author_facet | Jianhang Ma Wenjuan Zhang Andrea Marinoni Lianru Gao Bing Zhang |
| author_sort | Jianhang Ma |
| collection | DOAJ |
| description | The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale. |
| first_indexed | 2024-12-24T04:32:22Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T04:32:22Z |
| publishDate | 2018-08-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj.art-e758d2a23dd9429a90211e72a613e6fc2022-12-21T17:15:23ZengMDPI AGRemote Sensing2072-42922018-08-01109138810.3390/rs10091388rs10091388An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like ImagesJianhang Ma0Wenjuan Zhang1Andrea Marinoni2Lianru Gao3Bing Zhang4Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaCentre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA), Department of Physics and Technology, UiT-The Arctic University of Norway, Sykehusvegen 21, NO-9019 Tromsø, NorwayKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, ChinaThe trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.http://www.mdpi.com/2072-4292/10/9/1388spatiotemporal image fusionspatial-unmixingImproved Spatial and Temporal Reflectance Unmixing Model (ISTRUM)landsatSubstrate, Vegetation, and Dark surface (SVD) linear mixture model |
| spellingShingle | Jianhang Ma Wenjuan Zhang Andrea Marinoni Lianru Gao Bing Zhang An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images Remote Sensing spatiotemporal image fusion spatial-unmixing Improved Spatial and Temporal Reflectance Unmixing Model (ISTRUM) landsat Substrate, Vegetation, and Dark surface (SVD) linear mixture model |
| title | An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images |
| title_full | An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images |
| title_fullStr | An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images |
| title_full_unstemmed | An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images |
| title_short | An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images |
| title_sort | improved spatial and temporal reflectance unmixing model to synthesize time series of landsat like images |
| topic | spatiotemporal image fusion spatial-unmixing Improved Spatial and Temporal Reflectance Unmixing Model (ISTRUM) landsat Substrate, Vegetation, and Dark surface (SVD) linear mixture model |
| url | http://www.mdpi.com/2072-4292/10/9/1388 |
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