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Spatiotemporal Super-Resolution Mapping by Considering the Point Spread Function Effect
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DOI：10.1109/LGRS.2021.3050620.png)
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Abstract
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With the help of the auxiliary information provided by the appropriate prior fine spectral image (PFSI) in the same region, spatiotemporal super-resolution mapping (SSM) shows greater potential and better performance than the traditional super-resolution mapping (SM) models based on only monotemporal image. However, the temporal dependence of the existing SSM models usually describes the relationship between the coarse fractional images from original coarse spectral image (OCSI) and the fine fractional images from the PFSI, and the scale of temporal dependence information is not accurate and rich due to the different scales and properties of two fractional images. In addition, the existing SSM models usually do not consider point spread function (PSF) effect, resulting in affecting the accuracy of mapping result. To resolve the abovementioned issues, this letter proposes a general SSM model based on fine and coarse scales temporal dependence (FCSTD) by considering PSF effect. The experimental results demonstrate that the proposed model produces better mapping results than the traditional SM models, as well as the SSM models.
Keywords:
Superresolution
Spatiotemporal phenomena
Laboratories
Remote sensing
Mathematical model
Spatial resolution
Radar imaging
Point spread function (PSF)
spatiotemporal super-resolution mapping (SSM)
spectral image
super-resolution mapping (SM)
temporal dependence
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