Spectral unmixing based spatiotemporal downscaling fusion approach |
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| Institution: | 1. Department of Survey and Remote Sensing, School of Geosciences and Info-physics, Central South University, Changsha 410083, PR China;2. Center for Geomatics and Regional Sustainable Development Research, Central South University, Changsha 410083, PR China;3. Department of Civil Engineering, Ryerson University, Toronto, Ontario, M5B 2K3, Canada;4. Ministry of Transportation Ontario, Toronto, Ontario, M7A 2J3, Canada |
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| Abstract: | Time-series remote sensing data are important in monitoring land surface dynamics. Due to technical limitations, satellite sensors have a trade-off between temporal, spatial and spectral resolutions when acquiring remote sensing images. In order to obtain remote sensing images with high spatial resolution and high temporal frequency, spatiotemporal fusion methods have been developed. In this paper, we propose a Linear Spectral Unmixing-based Spatiotemporal Data Fusion Model (LSUSDFM) for spatial and temporal data fusion. In this model, the endmember abundance of the low-resolution image pixel is calculated based on that of the high-resolution image by the spectral mixture analysis. The endmember spectrum signals of low-resolution images are then calculated continuously within an optimized moving window. Subsequently, the fused image is reconstructed according to the endmember spectrum and its corresponding abundance map. A simulated dataset and real satellite images are used to test the fusion model, and the fusion results are compared with a current spectral unmixing based downscaling fusion model (SUDFM). Our experimental work shows that, compared to the SUDFM, the proposed LSUSDFM can achieve better quality and accuracy of fused images, especially in effectively eliminating the “plaque” phenomenon in the results by the SUDFM. The LSUSDFM has great potential in generating images with both high spatial resolution and high temporal frequency, as well as increasing the number of spectral bands of the high spatial resolution data. |
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| Keywords: | Remote sensing Spatiotemporal data fusion Downscaling Spectral mixture analysis Reflectance |
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