Denoising of time series data based on Bayes models using wavelets

点击翻译按钮获取中文摘要

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.