Thin Cloud Removal with Residual Symmetrical Concatenation Network

Abstract

Thin cloud removal is very important for optical remote sensing imagery. Different from thick cloud removal, the pixels contaminated by thin cloud still preserve some surface information. Therefore, thin cloud removal methods usually focus on suppressing the cloud influence instead of replacing the cloudy pixels. In this paper, we proposed a deep residual symmetrical concatenation network (RSC-Net) to make end-to-end cloud removal. The RSC-Net is based on an encoding-decoding framework consisting of multiple residual convolutional layers and residual deconvolutional layers. The feature maps of each convolutional layer are copied and concatenated to their symmetrical deconvolutional layers. Using supervised training with real Landsat-8 data, input samples include one cloudy image and one cloud-free reference image, and the cloud-free reference image also serves as the target. When the RSC-Net is fully trained, it is able to take cloudy image as input and produce cloud-free image as output. Experimental results show that our method has significant advantages in removing thin cloud contaminations in different bands when compared with other traditional and state-of-the-art methods.