Physics Informed Foundational Models for SAR Image Interpretation

Dear Colleagues,

With the rapid development of earth observation technology, high-resolution earth imaging capabilities have been achieved, and massive multi-modal high-resolution remote sensing image data can be obtained to provide basic data sources for downstream tasks. Remote sensing image interpretation has important application value fields such as resource survey, environmental monitoring, monitoring climate changes, natural hazards , disaster rescue or emergency response.

In the past two years, foundational models have become the most popular new technology in the field of artificial intelligence, including various foundational models such as CLIP, Grounding DINO, SAM, and SegGPT.

The foundational models have more powerful feature representation and learning capabilities, and can handle more diverse complex tasks and massive data. It can provide new technical methods and frameworks for remote sensing image interpretation tasks such as image classification, target detection, semantic segmentation, and change detection.

This special issue aims to explore the cutting-edge research and application of foundational models in the field of remote sensing image interpretation. We sincerely invite authors to submit relevant research articles to Remote Sensing to improve the cutting-edge development of remote sensing image interpretation technology.

Potential topics may include, but are not limited to, the following topics applied to SAR remote sensing:

• New large-scale remote sensing image dataset
• image classification based on foundational models
• environmental parameters extraction based on foundational models
• images semantic segmentation based on foundational models
• multitemporal analysis based on foundational models
• New methods for remote sensing image interpretation based on physics-aware foundational models
• New methods for multi-modal remote sensing image interpretation
• New method of remote sensing image interpretation based on efficient foundational models

Prof. Dr. Zhixiang Huang
Prof. Dr. Mihai Datcu
Dr. Jie Chen
Dr. Zhongling Huang
Guest Editors

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