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dc.contributor.authorMa, Jianhang
dc.contributor.authorZhang, Wenjuan
dc.contributor.authorMarinoni, Andrea
dc.contributor.authorGao, Lianru
dc.contributor.authorZhang, Bing
dc.date.accessioned2019-02-12T10:15:55Z
dc.date.available2019-02-12T10:15:55Z
dc.date.issued2018-08-31
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThe National Key R&D Program of China The National Natural Science Foundation of Chinaen_US
dc.descriptionSource at <a href=https://doi.org/10.3390/rs10091388> https://doi.org/10.3390/rs10091388</a>.en_US
dc.identifier.citationMa, J., Zhang, W., Marinoni, A., Gao, L. & Zhang, B. (2018). An improved spatial and temporal reflectance unmixing model to synthesize time series of landsat-like images. <i>Remote Sensing, 10</i>(9). https://doi.org/10.3390/rs10091388en_US
dc.identifier.cristinIDFRIDAID 1623247
dc.identifier.doi10.3390/rs10091388
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/14677
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalRemote Sensing
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.subjectspatiotemporal image fusionen_US
dc.subjectspatial-unmixingen_US
dc.subjectImproved Spatial and Temporal Reflectance Unmixing Model (ISTRUM)en_US
dc.subjectlandsaten_US
dc.subjectSubstrate, Vegetation, and Dark surface (SVD) linear mixture modelen_US
dc.titleAn improved spatial and temporal reflectance unmixing model to synthesize time series of landsat-like imagesen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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