DecRecNet: A Decoupling-Reconstruction Network for Restoring the Missing Information of Optical Remote Sensing Images

Abstract

Temporal-based methods effectively improve the utilization rate of remote sensing images but large ratios of missing information still need to be improved in the reconstruction models. In this article, based on the imaging theory with the help of a radiation correction model, a decoupling-reconstruction network (DecRecNet) for image reconstruction is proposed. The network uses a ground content radiation (GCR) correction module and imaging environment radiation (IER) correction module and their corresponding loss functions to decouple the image radiation information into GCR related to the ground objects and IER related to the imaging conditions and carry out targeted processing to achieve the purpose of missing information reconstruction. The GCR consistency loss function is used to preserve the ground content information, and the IER consistency loss function and IER smoothness loss are used to coordinate the imaging environment. The radiation-guiding module further performs targeted radiation adjustment on the foreground and background images to transfer the background imaging environment to the foreground and consistent radiation information of the same ground object. Compared with the classical U-Net, DeepLabV3+, RFR-Net, and spatial-temporal-spectral deep convolutional neural network methods, our model showed remarkable advantages in cloud occlusion and stripes of Landsat-8 (30 m), GaoFen-1 (2 m), and Landsat-7 (30 m) at various missing ratios, data sources, resolutions, and scenes, and achieving the goal of missing information reconstruction in large missing ratios.

Keywords

• Clouds and cloud shadows removal
• convolutional neural network
• image restoration
• missing information reconstruction
• radiation correction