Optimizing cloud motion estimation on the edge with phase correlation and optical flow
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
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DOI：
10.5194/amt-16-1195-2023
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发表时间：
2023-03
影响因子：
3.8
通讯作者：
B. Raut;P. Muradyan;R. Sankaran;R. Jackson;Seongha Park;Sean Shahkarami;Dario Dematties;Yongho Kim;Joseph Swantek;Neal Conrad;Wolfgang Gerlach;S. Shemyakin;P. Beckman;N. Ferrier;S. Collis
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作者：
B. Raut;P. Muradyan;R. Sankaran;R. Jackson;Seongha Park;Sean Shahkarami;Dario Dematties;Yongho Kim;Joseph Swantek;Neal Conrad;Wolfgang Gerlach;S. Shemyakin;P. Beckman;N. Ferrier;S. Collis
抽象。相位相关（PC）是一种从红外和可见光光谱图像中估计云运动矢量（CMV）的方法。通常，使用快速傅里叶变换在图像的小块中计算相移。在这项研究中，我们调查的性能和稳定性的blockwise PC方法，通过改变块的大小，帧间隔，和组合的红色，绿色，蓝色（RGB）通道从全天空成像仪（TSI）在美国大气辐射测量用户设施的南部大平原网站。我们发现，较短的帧间隔，其次是较大的块大小，负责稳定的估计的CMV，建议较高的自相关。RGB通道的选择对CMV的质量影响有限，在快速演变的低层云期间，红色和灰度图像比其他组合稍微更可靠。通过在Sage网络基础设施测试平台上实施优化算法，在不同图像分辨率下测试了CMV的稳定性。我们发现，加倍的帧速率优于四倍的图像分辨率在实现CMV稳定。CMVs与风数据的相关性在0.38-0.59的范围内具有95%的置信区间，尽管两个数据集都存在不确定性和局限性。与构造的数据和光流法的PC方法的比较表明，后处理的矢量场的CMV的质量有显着的影响。雨滴污染图像可以通过TSI镜在运动场中的旋转来识别。这项研究的结果是至关重要的边缘计算传感器系统的优化算法。
Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.