| Summary: | 碩士 === 國立清華大學 === 通訊工程研究所 === 100 === Hyperspectral umnixing is a process to extract the spectral signatures (endmembers) and
the corresponding fractions (abundance maps) from the observed hyperspectral data of
an area. Dimension reduction is a common, primary step in hypserspectral unmixing
with the benefit of reducing noise effect and computation complexity. The affine set
fitting [1] which provides the best representation to a given noisy hyperspectral data in the
least-squares error sense is used as the dimension reduction method in many endmember
extraction algorithms; however, the presence of outliers in the data has been proved to
severely degrade the accuracy of affine set fitting. In this thesis, unlike conventional
outlier detectors which may be sensitive to window settings, we propose a robust affine
set fitting (RASF) algorithm for joint dimension reduction and outlier detection without
any window setting. Given the number of outliers and endmembers in advance, the RASF
algorithm is to find a data-representative affine set from the noise-outlier corrupted data,
while making the effects of outliers minimum, in the least-squares error sense. The
proposed RASF algorithm is then combined with Neyman-Pearson hypothesis testing,
termed RASF-NP, to further estimate the number of outliers present in the data. By
using RASF-NP, we can discard the outlier pixels and find a robust affine set to improve
the consequent hyperspectral unmixing processing. Finally, we present simulations and
real data experiment (AVIRIS hyperspectral data taken from LCVF site, Nevada [2])
to demonstrate the superior performance and computation efficiency of our proposed
algorithm to some existing outlier detection algorithms.
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