Total Variation Regularized Weighted Tensor Ring Decomposition for Missing Data Recovery in High-Dimensional Optical Remote Sensing Images

Abstract

Due to sensor malfunction and atmosphere disturbances, high-dimensional optical remote sensing (HORS) images often suffer from information missing, such as dead pixels and thick clouds. Tensor decomposition methods have been used to estimate the missing data of HORS images. However, most existing models hardly consider the inherent properties and effective structural information of HORS images. To this end, we propose a novel total variation (TV) regularized weighted tensor ring (TR) decomposition model to recover the missing content of HORS images. The TR decomposition has the powerful low-rank (LR) representation ability to recover the HORS data by employing three low-dimensional tensors, i.e., TR factors. An initialization step and proper weights are designed to enhance the flexibility for exploring different LR properties of TR factors. To further preserve the spatial smoothness, the local spatial TV from three directions is incorporated into the TR decomposition framework. Furthermore, an augmented Lagrange multiplier (ALM) algorithm is designed for solving the resulting optimization problem. Experiments on HORS images demonstrate the performances of the proposed method over the current state-of-the-art baselines.