A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images
This incremental study provides insights for researchers in remote sensing and computer vision by evaluating matching methods for satellite image processing.
The paper compared the stereo-matching performance of a fast Convolutional Neural Network (fst-CNN) and Census on satellite images, finding that fst-CNN achieved better accuracy, error distribution, and completeness, though both methods performed within the same order of magnitude.
Stereo dense image matching can be categorized to low-level feature based matching and deep feature based matching according to their matching cost metrics. Census has been proofed to be one of the most efficient low-level feature based matching methods, while fast Convolutional Neural Network (fst-CNN), as a deep feature based method, has small computing time and is robust for satellite images. Thus, a comparison between fst-CNN and census is critical for further studies in stereo dense image matching. This paper used cost function of fst-CNN and census to do stereo matching, then utilized semi-global matching method to obtain optimized disparity images. Those images are used to produce digital surface model to compare with ground truth points. It addresses that fstCNN performs better than census in the aspect of absolute matching accuracy, histogram of error distribution and matching completeness, but these two algorithms still performs in the same order of magnitude.