Cloud Removal in Optical Remote Sensing Imagery Using Multiscale Distortion-Aware Networks
Weikang Yu, Xiaokang Zhang, Man-On Pun
Abstract
Cloud layer contamination is a common problem in optical remote sensing (RS) images. Deep-learning-based cloud removal from RS imagery has attracted increasing attention in recent years. However, it remains challenging to exploit useful multiscale cloud-aware representations from cloud imagery due to the lack of effective modeling of cloud distortion effects and the weak feature representation capabilities of networks. To circumvent these challenges, we propose a multiscale distortion-aware cloud removal (MSDA-CR) network consisting of multiple cloud-distortion-aware representation learning (CDARL) modules combined in a multiscale grid architecture. Specifically, cloud distortion control functions (CDCFs) are defined and incorporated into the CDARL modules to adaptively model the distortion effects induced by cloud interference in the imaging process, with learnable parameters for the exploitation of distortion-restored representations. These representations are further distilled across different scales in the MSDA-CR network and integrated based on an attention mechanism to restore cloud-free images while retaining the spatial structures of ground objects. Extensive experiments on visible and multispectral RS datasets confirm the effectiveness of the proposed MSDA-CR network.