Distortion Disentanglement and Knowledge Distillation for Satellite Image Restoration

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; p. 1
• Main Authors: Kandula, Praveen, Rajagopalan, A. N.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Algorithms; Atmospheric models; Availability; Datasets; Distillation; Distilling; Distortion; Distortion disentanglement; Image color analysis; Image quality; Image restoration; knowledge distillation; Methods; Neural networks; Reflectance; Restoration; satellite image restoration; Satellite imagery; Satellites; Semantics; Task analysis; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3220230

Satellite images are typically subject to multiple distortions. Different factors affect the quality of satellite images, including changes in atmosphere, surface reflectance, sun illumination, viewing geometries etc., limiting its application to downstream tasks. In supervised networks, the availab...