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Remote Sensing
Volume 17
Issue 24
10.3390/rs17244017
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images

by
Dingkai Wang
1,2,3,
Feng Wang
1,2,
Jingyi Cao
1,2,
Niangang Jiao
1,2,*,
Yuming Xiang
4,5,
Enze Zhu
1,2,3,
Jingxing Zhu
1,2 and
Hongjian You
1,2,3
1
Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
2
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
4
College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
5
Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(24), 4017; https://doi.org/10.3390/rs17244017
Submission received: 3 November 2025 / Revised: 10 December 2025 / Accepted: 11 December 2025 / Published: 12 December 2025
Versions Notes

Abstract

Disparity estimation from high-resolution satellite stereo images is critical for 3D reconstruction but remains challenging due to large disparities, complex structures, and textureless regions. To address this, we propose a Semantic-Aware Optical Flow (SAOF) framework for fine-grained disparity estimation, which enhances optical flow-based via a multi-level optimization incorporating sub-top pyramid re-PatchMatch, scale-adaptive matching windows, and multi-feature cost refinement. For improving the spatial consistency of the resulting disparity map, SAMgeo-Reg is utilized to produce semantic prototypes, which are used to build guidance embeddings for integration into the optical flow estimation process. Experiments on the US3D dataset demonstrate that SAOF outperforms state-of-the-art methods across challenging scenarios. It achieves an average endpoint error (EPE) of 1.317 and a D1 error of 9.09%.
Keywords: high-resolution satellite stereo images; disparity estimation; optical flow; semantic guidance high-resolution satellite stereo images; disparity estimation; optical flow; semantic guidance

Share and Cite

MDPI and ACS Style

Wang, D.; Wang, F.; Cao, J.; Jiao, N.; Xiang, Y.; Zhu, E.; Zhu, J.; You, H. SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images. Remote Sens. 2025, 17, 4017. https://doi.org/10.3390/rs17244017

AMA Style

Wang D, Wang F, Cao J, Jiao N, Xiang Y, Zhu E, Zhu J, You H. SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images. Remote Sensing. 2025; 17(24):4017. https://doi.org/10.3390/rs17244017

Chicago/Turabian Style

Wang, Dingkai, Feng Wang, Jingyi Cao, Niangang Jiao, Yuming Xiang, Enze Zhu, Jingxing Zhu, and Hongjian You. 2025. "SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images" Remote Sensing 17, no. 24: 4017. https://doi.org/10.3390/rs17244017

APA Style

Wang, D., Wang, F., Cao, J., Jiao, N., Xiang, Y., Zhu, E., Zhu, J., & You, H. (2025). SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images. Remote Sensing, 17(24), 4017. https://doi.org/10.3390/rs17244017

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