A study on application of flow velocity distribution measurement of drift ice of Okhotsk coast ice sea navigation using Radar images

（1）加快了流冰雷达图像运动分析的速度。为了自动获得流冰分布的时间变化，可以将块匹配方法应用于流冰雷达图像。然而，流冰分布的运动是复杂的，并且在通常的块匹配分析中存在很多误差。此外，不仅需要了解当前形势以确保安全的海上航线，而且还需要预测未来的形势。并且，需要理想地自动和真实的实现该处理。块匹配方法的分析结果可以用作预测处理的输入信息。然而，因为在通常的块匹配方法中计算量非常大，所以真实的时间处理是困难的。然后，为了提高本研究中流冰分析的速度和准确性，开发了块匹配的改进技术。（2）基于神经网络的海冰流速亚像元估计方法提出了一种基于神经网络的海冰流速亚像元估计方法。如果需要精确的流冰位移，需要亚像素估计最佳匹配位置。通过对询问区域内的相关峰应用插值方案来获得亚像素精度。传统上，高斯函数被用作峰值拟合函数。然而，子像素精度取决于偏置误差和随机误差两者。对于所有的应用图像，很难确定峰值拟合函数。然后，我们提出了使用神经网络的方法，它具有先验知识的应用图像。我们表明，所提出的技术取得了良好的效果，从雷达图像。

(1)Speed-up of movement analysis of drift ice radar imageTo get a time change in the drift ice distribution automatically the block match method can be applied to the drift ice radar image. However, the movement of the drift ice distribution is complex, and a lot of errors occur in a usual block match analysis. Moreover, it is necessary not only to understand a present situation to secure a safe sea route but also forecast the situation in the future. And, it is necessary to achieve this processing ideally automatically and in real time. An analytical result of the block match method can be used as input information on the forecast processing. However, because the amount of the calculation is very large in a usual block match method, real time processing is difficult. Then, to measure speeding up and making of the drift ice analysis highly accurate in this research, the improvement technique of the block match is developed.(2)Sub-pixel estimation method using neural networksA sub pixel estimation technique using neural networks has been presented for obtaining flow velocity distribution of sea ice. If we need precise displacement of the flow of the drift ice, sub-pixel estimation of best matching location is required. The sub-pixel accuracy is obtained by applying an interpolation scheme to the correlation peak within the interrogation area. Traditionally, the Gauss function has been used as peak-fitting function. However the sub-pixel accuracy is dependent upon both a bias error and a random error. It is very difficult to identify peak fitting function for all applied images. Then, we propose the method that uses neural networks which has a priori knowledge about applied image. We show the proposed technique achieved good results from the radar image.