2005 Fiscal Year Final Research Report Summary
A study on application of flow velocity distribution measurement of drift ice of Okhotsk coast ice sea navigation using Radar images
| Project/Area Number |
16510131
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | Kushiro National College of Technology |
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Principal Investigator |
TAKAGI Toshiyuki Kushiro National College of Technology, Dept. of Electrical Engineering, Dr., Prof., 電気工学科, 教授 (30331953)
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| Project Period (FY) |
2004 – 2005
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| Keywords | Okhotsk sea / Radar image / Block matching method / Sub pixel / Neural networks |
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| Research Abstract |
(1)Speed-up of movement analysis of drift ice radar image
To 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 networks
A 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.
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Research Products
(4 results)
