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Remote Sensing
Volume 18
Issue 1
10.3390/rs18010082
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

SAREval: A Multi-Dimensional and Multi-Task Benchmark for Evaluating Visual Language Models on SAR Image Understanding

by
Ziyan Wang
1,2,
Lei Liu
1,2,*,
Gang Wan
1,2,
Yuchen Lu
1,
Fengjie Zheng
1,
Guangde Sun
1,
Yixiang Huang
1,
Shihao Guo
1,
Xinyi Li
1,2 and
Liang Yuan
1
1
Space Information Academic, Space Engineering University, Beijing 101407, China
2
Key Laboratory of Intelligent Processing and Application Technology of Satellite Information, Beijing 100192, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(1), 82; https://doi.org/10.3390/rs18010082
Submission received: 26 November 2025 / Revised: 19 December 2025 / Accepted: 23 December 2025 / Published: 25 December 2025
(This article belongs to the Special Issue Advanced Methods and Applications in SAR (Synthetic Aperture Radar) Image Target Detection and Recognition)
Versions Notes

Abstract

Vision-Language Models (VLMs) demonstrate significant potential for remote sensing interpretation through multimodal fusion and semantic representation of imagery. However, their adaptation to Synthetic Aperture Radar (SAR) remains challenging due to fundamental differences in imaging mechanisms and physical properties compared to optical remote sensing. SAREval, the first comprehensive benchmark specifically designed for SAR image understanding, incorporates SAR-specific characteristics, including scattering mechanisms and polarization features, through a hierarchical framework spanning perception, reasoning, and robustness capabilities. It encompasses 20 tasks from image classification to physical-attribute inference with over 10,000 high-quality image–text pairs. Extensive experiments conducted on 11 mainstream VLMs reveal substantial limitations in SAR image interpretation. Models achieve merely 25.35% accuracy in fine-grained ship classification tasks and demonstrate significant difficulties in establishing mappings between visual features and physical parameters. Furthermore, certain models exhibit unexpected performance improvements under certain noise conditions that challenge conventional robustness understanding. SAREval establishes an essential foundation for developing and evaluating VLMs in SAR image interpretation, providing standardized assessment protocols and quality-controlled annotations for cross-modal remote sensing research.
Keywords: Visual Language Models (VLMs); Synthetic Aperture Radar (SAR); image interpretation; benchmark; multimodal fusion Visual Language Models (VLMs); Synthetic Aperture Radar (SAR); image interpretation; benchmark; multimodal fusion

Share and Cite

MDPI and ACS Style

Wang, Z.; Liu, L.; Wan, G.; Lu, Y.; Zheng, F.; Sun, G.; Huang, Y.; Guo, S.; Li, X.; Yuan, L. SAREval: A Multi-Dimensional and Multi-Task Benchmark for Evaluating Visual Language Models on SAR Image Understanding. Remote Sens. 2026, 18, 82. https://doi.org/10.3390/rs18010082

AMA Style

Wang Z, Liu L, Wan G, Lu Y, Zheng F, Sun G, Huang Y, Guo S, Li X, Yuan L. SAREval: A Multi-Dimensional and Multi-Task Benchmark for Evaluating Visual Language Models on SAR Image Understanding. Remote Sensing. 2026; 18(1):82. https://doi.org/10.3390/rs18010082

Chicago/Turabian Style

Wang, Ziyan, Lei Liu, Gang Wan, Yuchen Lu, Fengjie Zheng, Guangde Sun, Yixiang Huang, Shihao Guo, Xinyi Li, and Liang Yuan. 2026. "SAREval: A Multi-Dimensional and Multi-Task Benchmark for Evaluating Visual Language Models on SAR Image Understanding" Remote Sensing 18, no. 1: 82. https://doi.org/10.3390/rs18010082

APA Style

Wang, Z., Liu, L., Wan, G., Lu, Y., Zheng, F., Sun, G., Huang, Y., Guo, S., Li, X., & Yuan, L. (2026). SAREval: A Multi-Dimensional and Multi-Task Benchmark for Evaluating Visual Language Models on SAR Image Understanding. Remote Sensing, 18(1), 82. https://doi.org/10.3390/rs18010082

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