Ge Yanqin, Li Yanrong, Chen Jinyong, Sun Kang, Li Dacheng, Han Qijin
Department of Earth Science and Engineering, Taiyuan University of Technology, China.
Lab of Aerospace System and Application, The 54th Research Institute of China Electronics Technology Group Corporation, China.
Sensors (Basel). 2020 Mar 24;20(6):1789. doi: 10.3390/s20061789.
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).
由于在卫星传感器当前的观测条件下，相关应用对高空间分辨率时间序列遥感影像的需求难以得到满足，因此在指定日期重建高分辨率影像成为关键。作为一种有效的数据重建技术，时空融合可用于生成具有明确地球物理意义的时间序列地表参数。在本研究中，基于基于稀疏表示的时空反射率融合模型（SPSTFM）开发了一种改进的融合模型，并用高分二号多光谱（GF-2 MS）和高分一号宽幅相机（GF-1 WFV）的反射率数据进行了评估。通过在字典训练和稀疏编码过程中引入空间增强训练方法，期望所开发的融合框架能提升对高分辨率和低分辨率过完备字典的描述。随后使用包括平均绝对偏差（AAD）、均方根误差（RMSE）、峰值信噪比（PSNR）、相关系数（CC）、光谱角映射器（SAM）、结构相似性（SSIM）和相对全局无量纲合成误差（ERGAS）在内的评估指标，对所采用的融合方法进行测试以进行平行比较。实验结果表明，与SPSTFM相比，能获得对GF-2 MS反射率更准确的预测，并且与诸如空间和时间自适应融合模型（STARFM）和增强型STARFM（ESTARFM）等流行的基于双对的反射率融合模型相当。
