Remote Sensing (May 2019)
Evaluation of Matching Costs for High-Quality Sea-Ice Surface Reconstruction from Aerial Images
Abstract
Satellite remote sensing can be used effectively with a wide coverage and repeatability in large-scale Arctic sea-ice analysis. To produce reliable sea-ice information, satellite remote-sensing methods should be established and validated using accurate field data, but obtaining field data on Arctic sea-ice is very difficult due to limited accessibility. In this situation, digital surface models derived from aerial images can be a good alternative to topographical field data. However, to achieve this, we should discuss an additional issue, i.e., that low-textured surfaces on sea-ice can reduce the matching accuracy of aerial images. The matching performance is dependent on the matching cost and search window size used. Therefore, in order to generate high-quality sea-ice surface models, we first need to examine the influence of matching costs and search window sizes on the matching performance on low-textured sea-ice surfaces. For this reason, in this study, we evaluate the performance of matching costs in relation to changes of the search window size, using acquired aerial images of Arctic sea-ice. The evaluation concerns three factors. The first is the robustness of matching to low-textured surfaces. Matching costs for generating sea-ice surface models should have a high discriminatory power on low-textured surfaces, even with small search windows. To evaluate this, we analyze the accuracy, uncertainty, and optimal window size in terms of template matching. The second is the robustness of positioning to low-textured surfaces. One of the purposes of image matching is to determine the positions of object points that constitute digital surface models. From this point of view, we analyze the accuracy and uncertainty in terms of positioning object points. The last is the processing speed. Since the computation complexity is also an important performance indicator, we analyze the elapsed time for each of the processing steps. The evaluation results showed that the image domain costs were more effective for low-textured surfaces than the frequency domain costs. In terms of matching robustness, the image domain costs showed a better performance, even with smaller search windows. In terms of positioning robustness, the image domain costs also performed better because of the lower uncertainty. Lastly, in terms of processing speed, the PC (phase correlation) of the frequency domain showed the best performance, but the image domain costs, except MI (mutual information), were not far behind. From the evaluation results, we concluded that, among the compared matching costs, ZNCC (zero-mean normalized cross-correlation) is the most effective for sea-ice surface model generation. In addition, we found that it is necessary to adjust search window sizes properly, according to the number of textures required for reliable image matching on sea-ice surfaces, and that various uncertainties due to low-textured surfaces should be considered to determine the positions of object points.
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