Image Restoration for Remote Sensing: Overview and Toolbox

TL;DR

This paper reviews recent advances in remote sensing image restoration, focusing on SAR and hyperspectral data, and provides a toolbox to facilitate further research and application in the field.

Contribution

It offers a comprehensive overview of restoration techniques specific to different sensor types and introduces a practical toolbox for researchers and students to explore these methods.

Findings

Focus on SAR and hyperspectral image restoration techniques.

Provides a discipline-specific overview with detailed references.

Includes a publicly available toolbox to support research and education.

Abstract

Remote sensing provides valuable information about objects or areas from a distance in either active (e.g., RADAR and LiDAR) or passive (e.g., multispectral and hyperspectral) modes. The quality of data acquired by remotely sensed imaging sensors (both active and passive) is often degraded by a variety of noise types and artifacts. Image restoration, which is a vibrant field of research in the remote sensing community, is the task of recovering the true unknown image from the degraded observed image. Each imaging sensor induces unique noise types and artifacts into the observed image. This fact has led to the expansion of restoration techniques in different paths according to each sensor type. This review paper brings together the advances of image restoration techniques with particular focuses on synthetic aperture radar and hyperspectral images as the most active sub-fields of image restoration in the remote sensing community. We, therefore, provide a comprehensive, discipline-specific starting point for researchers at different levels (i.e., students, researchers, and senior researchers) willing to investigate the vibrant topic of data restoration by supplying sufficient detail and references. Additionally, this review paper accompanies a toolbox to provide a platform to encourage interested students and researchers in the field to further explore the restoration techniques and fast-forward the community. The toolboxes are provided in https://github.com/ImageRestorationToolbox.