A GF-based multi-source multi-temporal satellite benchmark for binocular disparity estimation

As the cost of small satellites decreases annually and their performance improves, hundreds of high-resolution satellites are now in orbit. This has increased the demand for satellite stereo processing toward detailed observations of the Earth. However, satellites are limited by their orbit, making it difficult to observe all surfaces of a target. To address this, multiple satellites can be used for collaborative observation. However, imaging quality and timing vary across different satellites, making multi-source and multi-temporal stereo data processing a key research focus. To advance this area, this study developed a high-precision digital surface model (DSM) and satellite images to create a multi-source and multi-temporal satellite stereo matching dataset, verifying the accuracy of the dataset. The dataset demonstrated row and disparity accuracies better than one pixel for the epipolar images. Consequently, our dataset supports algorithm testing and model training, enhancing multi-satellite collaborative observations.