4.7 Article

Spatiotemporal Super-Resolution Mapping by Considering the Point Spread Function Effect

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3050620

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

Funding

  1. National Natural Science Foundation of China [61801211, 61871218]
  2. Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing [KLIGIP-2019A05]
  3. Key Laboratory of Intelligent Optimization and Information Processing, Minnan Normal University [ZNYH202006]
  4. Open Project Program of Hubei Key Laboratory of Regional Development and Environment Response Fundamental [2020(B)004]
  5. Open Project Program of State Key Laboratory of Geo-Information Engineering [SKLGIE2019-M-3-4]
  6. Fundamental Research Funds for the Central Universities [NZ2020009]

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This study proposed a general SSM model based on FCSTD, considering PSF effect, to improve mapping results using auxiliary information from PFSI in the same region.
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.

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