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Stereo dense image matching can be categorized to low-level feature basedmatching and deep feature based matching according to their matching costmetrics. Census has been proofed to be one of the most efficient low-levelfeature based matching methods, while fast Convolutional Neural Network(fst-CNN), as a deep feature based method, has small computing time and isrobust for satellite images. Thus, a comparison between fst-CNN and census iscritical for further studies in stereo dense image matching. This paper usedcost function of fst-CNN and census to do stereo matching, then utilizedsemi-global matching method to obtain optimized disparity images. Those imagesare used to produce digital surface model to compare with ground truth points.It addresses that fstCNN performs better than census in the aspect of absolutematching accuracy, histogram of error distribution and matching completeness,but these two algorithms still performs in the same order of magnitude.

A Comparison of Stereo-Matching Cost between Convolutional Neural Network and Census for Satellite Images