Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing (2nd Edition)

Dear Colleagues,

Remote sensing image processing plays a critical role in diverse fields such as environmental monitoring, resource management, and disaster response. However, processing and analyzing remotely sensed data can be challenging due to complex environments, limited signal-to-noise ratios, and the presence of noise and artifacts. In recent years, two differing approaches to remote sensing image processing have emerged: knowledge-driven and data-driven methods. The knowledge-driven methods, based on expert experience or mathematical models that describe the physical processes underlying remote sensing data, exhibit high interpretability. In contrast, data-driven methods leverage machine learning algorithms to identify correlations and patterns from observed data, and have become prevalent in recent years. Therefore, this Special Issue focuses on exploring the advantages and limitations of knowledge-driven and data-driven approaches and suggests ways to combine them to enhance remote sensing image processing. We hope to receive a variety of both theoretical or heuristic works on this topic, leverage the strengths of knowledge-driven and data-driven methods, and provide valuable insights into the development of enhanced remote sensing techniques for a broad range of applications.

The scope of this Special Issue includes, but is not limited to, the following topics:

1. General remote sensing image processing, such as classification, object detection, segmentation, super-resolution, denoising, etc.
2. Real-world applications based on remote sensing images, such as land use mapping, vegetation analysis, and environmental monitoring.
3. Combining traditional methods and deep learning methods for remote sensing image processing and analysis.
4. Multi-modal remote sensing image processing, such as multi-modal image fusion, pan-sharpening, etc.

Prof. Dr. Junmin Liu
Prof. Dr. Xile Zhao
Prof. Dr. Bin Zhao
Guest Editors

Manuscript Submission Information

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