2,935 research outputs found
PCA detection and denoising of Zeeman signatures in stellar polarised spectra
Our main objective is to develop a denoising strategy to increase the signal
to noise ratio of individual spectral lines of stellar spectropolarimetric
observations.
We use a multivariate statistics technique called Principal Component
Analysis. The cross-product matrix of the observations is diagonalized to
obtain the eigenvectors in which the original observations can be developed.
This basis is such that the first eigenvectors contain the greatest variance.
Assuming that the noise is uncorrelated a denoising is possible by
reconstructing the data with a truncated basis. We propose a method to identify
the number of eigenvectors for an efficient noise filtering.
Numerical simulations are used to demonstrate that an important increase of
the signal to noise ratio per spectral line is possible using PCA denoising
techniques. It can be also applied for detection of magnetic fields in stellar
atmospheres. We analyze the relation between PCA and commonly used well-known
techniques like line addition and least-squares deconvolution. Moreover, PCA is
very robust and easy to compute.Comment: accepted to be published in A&
Robust statistical approaches for feature extraction in laser scanning 3D point cloud data
Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction
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