| Project/Area Number |
11640120
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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 |
General mathematics (including Probability theory/Statistical mathematics)
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| Research Institution | HIROSHIMA UNIVERSITY |
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Principal Investigator |
NISHII Ryuei Hiroshima University, Integrated Arts and Sciences, Professor, 総合科学部, 教授 (40127684)
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| Co-Investigator(Kenkyū-buntansha) |
KUWADA Masahide Hiroshima University, Faculty of Integrated Arts and Science, Professor, 総合科学部, 教授 (10144891)
TANAKA Shojiro Shimane University, Interdisciplinary Faculty of Science and Engineering, Professor, 総合理工学部, 教授 (00197427)
FUJIKOSHI Yasunori Hiroshima University, Graduate School of Science, Professor, 大学院・理学研究科, 教授 (40033849)
ASANO Akira Hiroshima University, Faculty of Integrated Arts and Sciences, Associate Professor, 総合科学部, 助教授 (60243987)
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| Project Period (FY) |
1999 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 2000: ¥1,300,000 (Direct Cost: ¥1,300,000)
Fiscal Year 1999: ¥1,800,000 (Direct Cost: ¥1,800,000)
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| Keywords | cokriging / data fusion / filtering / penalized likelihood / shrinkage estimate / smoothing / unmixing / deforestation / penalized likelibood / segrnentation / Bayes linear model / Denoising / Low-pass filter / Shrinkage estimator / Wavelet matrix |
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| Research Abstract |
The main aim of this study is to reduce noise terms in time series data. We transform the time-series data by the orthogonal matrix due to orthogonal wavelets and obtain wavelet coefficients. Assuming the coefficients follow Bayes models, we keep/shrink/kill the coefficients through the trade-off parameter. It is shown through various numerical examples that the procedure is superior to those proposed in the literature.
Our other results related to the satellite data analysis are as follows :
- We discuss Landsat data analysis from various aspects ; history of Landsat, data acqusition system, geometric correction, denoising, data fusion and discriminant analysis. We reviewed and newly observed these points, and publish them in book form.
- Concerning land-cover classifications, we consider spatially-correlated normal distributions and develop parameter estimation procedure. The multispectrum data are discriminated by a penalized likelihood due to Mahalanobis distance. The classifier can utilize mixel traing data as well as pure cells. By numerical study, the classifier works well, especially in unmixing case.
- Concerning data fusion techniques, we consider a method based on cokriging. It is shown that the method shows better performance than HSV method which is widely used for enhancement of low-resolution colored images. We also examined variable selection criterion based on AIC for prediction of low-resolution images.
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