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Open AccessArticle

A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data

1
Department of Earth Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Lab of Aerospace System and Application, The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
3
Department of Surveying and Mapping, Taiyuan University of Technology, Taiyuan 030024, China
4
Department of Remote Sensing Calibration, China Centre for Resources Satellite Data and Application, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1789; https://doi.org/10.3390/s20061789
Received: 6 February 2020 / Revised: 17 March 2020 / Accepted: 19 March 2020 / Published: 24 March 2020
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Since requirements of related applications for time series remotely-sensed images with high spatial resolution have been hard to be satisfied under current observation conditions of satellite sensors, it is key to reconstruct high-resolution images at specified dates. As an effective data reconstruction technique, spatiotemporal fusion can be used to generate time series land surface parameters with a clear geophysical significance. In this study, an improved fusion model based on the Sparse Representation-Based Spatiotemporal Reflectance Fusion Model (SPSTFM) is developed and assessed with reflectance data from Gaofen-2 Multi-Spectral (GF-2 MS) and Gaofen-1 Wide-Field-View (GF-1 WFV). By introducing a spatially enhanced training method to dictionary training and sparse coding processes, the developed fusion framework is expected to promote the description of high-resolution and low-resolution overcomplete dictionaries. Assessment indices including Average Absolute Deviation (AAD), Root-Mean-Square Error (RMSE), Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), spectral angle mapper (SAM), structure similarity (SSIM) and Erreur Relative Global Adimensionnelle de Synthèse (ERGAS) are then used to test employed fusion methods for a parallel comparison. The experimental results show that more accurate prediction of GF-2 MS reflectance than that from the SPSTFM can be obtained and furthermore comparable with popular two-pair based reflectance fusion models like the Spatial and Temporal Adaptive Fusion Model (STARFM) and the Enhanced-STARFM (ESTARFM). View Full-Text
Keywords: sparse-representation; spatiotemporal fusion; SPSTFM; two image-pair; GF-2 and GF-1 WFV images sparse-representation; spatiotemporal fusion; SPSTFM; two image-pair; GF-2 and GF-1 WFV images
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Ge, Y.; Li, Y.; Chen, J.; Sun, K.; Li, D.; Han, Q. A Learning-Enhanced Two-Pair Spatiotemporal Reflectance Fusion Model for GF-2 and GF-1 WFV Satellite Data. Sensors 2020, 20, 1789.

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