MSC-GAN: A Multistream Complementary Generative Adversarial Network With Grouping Learning for Multitemporal Cloud Removal

Optical remote sensing images have extensive application value, but cloud contamination greatly limits their potential use in the field of geographic information. Cloud removal aims to restore clear, unobstructed images from cloud-covered ones for subsequent in-depth analysis. Due to severe cloud cover problems such as thick clouds in some areas of remote sensing images, cloud removal tasks have become challenging. Recently, many methods have attempted to incrementally fill in obscured regions by fusing cloud-free information from multitemporal data. However, most of these methods fail to effectively utilize the interaction among different temporal data, and some information of data is easily lost in the process of deep transmission, this causes problems such as inadequate cloud removal and blurred recovery of ground under the clouds. Therefore, we propose a multistream complementary generative adversarial network (MSC-GAN) for cloud removal using multitemporal data. First, it employs a multistream complementary (MSC) architecture in the down-sampling feature encoding stage to effectively promote the interaction of feature information across multitemporal data, alleviating information loss as network depth increases. Second, to reduce the feature blur, we design a group feature reweighting (GFR) module as a complementary connection of long-distance information, in which the grouping learning and multidimensional parallel architecture can cost-effectively enhance semantic fusion between low-level and high-level features. Moreover, a channel enhancement method is introduced to assist in processing the underlying transition information, minimizing the interference of invalid information. Experimental results on multiple benchmark datasets under a series of image quality assessment metrics demonstrate the effectiveness of the proposed method.