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Principal component analysis (PCA) is an important method for feature extraction of hyperspectral remote sensing image. With the development of hyperspectral sensors, the magnitude of hyperspectral data grows quickly, and it is a challenging task to efficiently reduce the data dimension and compress massive data volumes in hyperspectral imaging. In this paper, a distributed parallel optimization of...
Linear spectral unmixing aims at estimating the number of pure spectral substances, also called <bold>endmembers</bold>, their spectral signatures, and their abundance fractions in remotely sensed hyperspectral images. This paper describes a method for unsupervised hyperspectral unmixing called minimum volume simplex analysis (MVSA) and introduces a new computationally efficient implementation...
Fast independent component analysis (Fast ICA) for hyper spectral image dimensionality reduction is computationally complex and time-consuming due to the high dimensionality of hyper spectral images. By analyzing the Fast ICA algorithm, we design parallel schemes for covariance matrix calculating, white processing and ICA iteration at three parallel levels: multicores, many integrated cores (MIC),...
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