Machine Learning for Remote Sensing Image Recovery and Earth Observation Applications

Dear Colleagues,

Remote sensing technology has revolutionized Earth observation by offering continuous and multi-scale monitoring of natural and human-induced processes. However, remote sensing images are often affected by noise, clouds, and resolution limitations that hinder accurate interpretation. With the rapid development of machine learning, advanced algorithms show great potential in restoring and enhancing remote sensing data quality, thereby improving their usability for geoscientific research.

This Special Issue aims to present recent advances in machine learning methods for remote sensing image recovery and their applications in Earth and environmental sciences. We invite contributions that integrate algorithmic innovation with real-world geoscientific challenges, including land surface change detection, geological mapping, hydrological and coastal monitoring, and ecosystem assessment.

Topics include, but are not limited to, the following:

Machine learning for image denoising, deblurring, and cloud removal in remote sensing;

Super-resolution and image reconstruction for geological and environmental monitoring;

Self-supervised and physics-informed learning for data recovery in Earth observation;

Multimodal data fusion and enhancement for geoscience applications;

Benchmark datasets and validation for restored remote sensing products.

Dr. Qiang Zhang
Dr. Yi Xiao
Dr. Xiangyong Cao
Dr. Yong Chen
Dr. Jize Xue
Guest Editors

Manuscript Submission Information

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