Cloud contamination is a common problem in Earth observation that hinders various remote sensing applications.To address this problem, recent studies have employed deep neural networks and multi-modal data fusion to reconstruct cloud-free optical imagery.However, this task faces many challenges, such as: (1) the scarcity of suitable multi-modal datasets; (2) the ineffective use of feature correlations; and (3) the limited applicability of existing models.To overcome these challenges, this study proposes a novel solution that fuses high-spatial SAR and low-spatial optical Optimal Management of Thermal Comfort and Driving Range in Electric Vehicles data to reconstruct high-quality cloud-free multi-spectral optical products.First, a curated benchmark dataset, named SMILE-CR, is created with a realistic cloud simulation strategy.
The SMILE-CR serves as a global and multi-modal cloud removal dataset for the Landsat-8 sensor, with Sentinel-1 and MODIS data as additional supplementary data.Second, a Transformer-based cloud removal network, abbreviated as CRformer, is developed with two novel modules: multi-head dense and sparse attention and multi-scale gated-dconv feed-forward network.The CRformer achieves global attention while suppressing the weak correlations PERAN INOVASI PRODUK MEMEDIASI ORIENTASI PASAR TERHADAP KINERJA PEMASARAN and enhancing the multi-scale cloud features by filtering out invalid features.The performance of the proposed method is evaluated through extensive experiments.The results show that the CRformer surpasses the state-of-the-art cloud removal methods with significant improvements in both quantitative and qualitative metrics.
The fusion of MODIS and Sentinel-1 data is shown to be effective and necessary in reconstructing Landsat-8 observations.Moreover, the CRformer model can be readily applied to reconstruct time-series cloud-free Landsat-8 products in Wuhan city, which can improve the average accuracy of land cover mapping by over 3%.