SAOF: A Semantic-Aware Optical Flow Framework for Fine-Grained Disparity Estimation in High-Resolution Satellite Stereo Images
Year: 2025
Type: article
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%.
Source: Remote Sensing
DOI: 10.3390/rs17244017
Authors: Dingkai Wang, Feng Wang, Jingyi Cao, Niangang Jiao, Yuming Xiang, Enze Zhu, Jingxing Zhu, Hongjian You
Language: English
Topic: Advanced Vision and Imaging
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W4417278601
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