Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing

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 ratio, and the presence of noise and artifacts. Recently, two different approaches to remote sensing image processing have emerged: knowledge-driven and data-driven methods. Among these, the knowledge-driven methods, based on expert experience or mathematical models describing the physical processes underlying remote sensing data, show high interpretability. In contrast, data-driven methods leverage machine learning algorithms to identify correlations and patterns from observed data, which are prevalent in recent years. In particular, this Special Issue focuses on exploring the advantages and limitations of knowledge-driven and data-driven approaches and suggesting ways to combine them to boost remote sensing image processing. We are looking forward to receiving a variety of works on this topic, whether they are theoretical or heuristic. This Special Issue is expected to leverage the strengths of knowledge-driven and data-driven methods and provide valuable insights into developing better remote sensing techniques for a broad range of applications.

Topics of interest include, but are not limited to, the following points:

• General remote sensing image processing, such as classification, object detection, segmentation, super-resolution, denoising, etc.
• Real-world applications based on remote sensing images, such as land use mapping, vegetation analysis, and environmental monitoring.
• Combining traditional methods and deep learning methods for remote sensing image processing and analysis.
• 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. Tieyong Zeng
Dr. Bin Zhao
Dr. Claudia Paris
Guest Editors

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