Joint robust outlier detection and dimension reduction for hyperspectral image analysis

• Main Authors: Huang, Hao-En, 黃浩恩
• Other Authors: Chi, Chong-Yung
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/72615609986170212329

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.