A Unified Spatial-Temporal-Spectral Learning Framework for Reconstructing Missing Data in Remote Sensing Images

摘要

In this paper, a unified spatial-temporal-spectral framework of missing information reconstruction in remote sensing images is proposed. Based on an end-to-end non-linear mapping structure, the proposed method employs a unified deep convolutional neural network combined with joint spatial-temporal-spectral supplementary information. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of real-data experiments demonstrate that the proposed model exhibits high effectiveness in missing information reconstruction tasks like dead lines in Aqua MODIS band 6, Landsat ETM+ SLC-off and thick cloud removal.