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Remote Sensing
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Physics Informed Foundational Models for SAR Image Interpretation
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Physics Informed Foundational Models for SAR Image Interpretation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 509

Special Issue Editors

Prof. Dr. Zhixiang Huang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Anhui University, Hefei, China
Interests: theoretical and computational research in electromagnetics and image; intelligent interpretation of SAR targets
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mihai Datcu

E-Mail Website
Guest Editor
Faculty of Electronics, Telecommunications and Information Technology, University POLITEHNICA of Bucharest (UPB), 006042 Bucharest, Romania
Interests: information theory; signal processing; SAR systems; explainable and physics-aware artificial intelligence; computational imaging; quantum machine learning with applications in EO
Special Issues, Collections and Topics in MDPI journals
Dr. Jie Chen

E-Mail Website
Guest Editor
Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
Interests: synthetic aperture radar (SAR) image interpretation; efficient deep learning; neural architecture search
Dr. Zhongling Huang

E-Mail Website
Guest Editor
School of Automation, Northwestern Polytechnical University, Xi'an, China
Interests: explainable deep learning for synthetic aperture radar (XAI4SAR); synthetic aperture radar (SAR) image interpretation; deep learning; remote sensing data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SAR
  • foundational models
  • physics-aware foundational models
  • efficient foundational models
  • climate change monitoring and adaptation
  • semantic segmentation
  • target detection and classification
  • change detection
  • multi-modal remote sensing image

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Published Papers (1 paper)

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Research

19 pages, 10070 KiB  
Article
SAR Image Target Segmentation Guided by the Scattering Mechanism-Based Visual Foundation Model
by Chaochen Zhang, Jie Chen, Zhongling Huang, Hongcheng Zeng, Zhixiang Huang, Yingsong Li, Hui Xu, Xiangkai Pu and Long Sun
Remote Sens. 2025, 17(7), 1209; https://doi.org/10.3390/rs17071209 - 28 Mar 2025
Viewed by 228
Abstract
As a typical visual foundation model, SAM has been extensively utilized for optical image segmentation tasks. However, synthetic aperture radar (SAR) employs a unique imaging mechanism, and its images are very different from optical images. Directly transferring a pretrained SAM from optical scenes [...] Read more.
As a typical visual foundation model, SAM has been extensively utilized for optical image segmentation tasks. However, synthetic aperture radar (SAR) employs a unique imaging mechanism, and its images are very different from optical images. Directly transferring a pretrained SAM from optical scenes to SAR image instance segmentation tasks can lead to a substantial decline in performance. Therefore, this paper fully integrates the SAR scattering mechanism, and proposes a SAR image target segmentation method guided by the SAR scattering mechanism-based visual foundation model. First, considering the discrete distribution features of strong scattering points in SAR imagery, we develop an edge enhancement morphological adaptor. This adaptor is designed to incorporate a limited set of trainable parameters aimed at effectively boosting the target’s edge morphology, allowing quick fine-tuning within the SAR realm. Second, an adaptive denoising module based on wavelets and soft-thresholding techniques is implemented to reduce the impact of SAR coherent speckle noise, thus improving the feature representation performance. Furthermore, an efficient automatic prompt module based on a deep object detector is built to enhance the ability of rapid target localization in wide-area scenes and improve image segmentation performance. Our approach has been shown to outperform current segmentation methods through experiments conducted on two open-source datasets, SSDD and HRSID. When the ground-truth is used as a prompt, SARSAM improves mIOU by more than 10%, and APmask50 by more than 5% from the baseline. In addition, the computational cost is greatly reduced because the number of parameters and FLOPs of the structures that require fine-tuning are only 13.5% and 10.1% of the baseline, respectively. Full article
(This article belongs to the Special Issue Physics Informed Foundational Models for SAR Image Interpretation)
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