Q. Zhang, Q. Yuan, C. Zeng, X. Li, and Y. Wei, “Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network,” IEEE TGRS, 2018.
In your paper you explained that loss function is the mean squared error. MSE between the residual mapping and is the other one the output from the model directly?
As stated in your paper, “For the dead lines in Aqua MODIS band 6, input y1 is the spectral data with missing information, and input y2 is the other intact spectral data as auxiliary information. For the ETM+ SLC-off problem, input y1 is the temporal image with missing information, and input y2 is another temporal image. For removing thick cloud in remote sensing imagery, input y1 is a temporal image with regions covered by thick cloud, and input y2 is another temporal image without cloud.”,why you fuse spatial,spectral,temporal features in this way? For example, for dead line, why you don't fuse temporal data and fuse spatial and spectral features only?