Missing Information Reconstruction of Remote Sensing Data: A Technical Review

作者：Huanfeng Shen, Xinghua Li, Qing Cheng, Chao Zeng, Gang Yang, Huifang Li, Liangpei Zhang · 发表于：IEEE Geoscience and Remote Sensing Magazine · 年份：2015 · DOI：10.1109/mgrs.2015.2441912 · 被引用次数：585 · 研究领域：Remote-Sensing Image Classification、Remote Sensing in Agriculture、Advanced Image Fusion Techniques

Because of sensor malfunction and poor atmospheric conditions, there is usually a great deal of missing information in optical remote sensing data, which reduces the usage rate and hinders the follow-up interpretation. In the past decades, missing information reconstruction of remote sensing data has become an active research field, and a large number of algorithms have been developed. However, to the best of our knowledge, there has not, to date, been a study that has been aimed at expatiating and summarizing the current situation. This is therefore our motivation in this review. This paper provides an introduction to the principles and theories of missing information reconstruction of remote sensing data. We classify the established and emerging algorithms into four main categories, followed by a comprehensive comparison of them from both experimental and theoretical perspectives. This paper also predicts the promising future research directions.