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Hyperspectral endmember extraction is a process to extract end-member signatures from the observed hyperspectral data of an area. The presence of outliers in the data has been proved to pose a serious problem in endmember extraction. In this paper, unlike conventional outlier detectors which may be sensitive to window settings, we propose a robust affine set fitting (RASF) algorithm for joint dimension...
In this paper, we describe a continuous optimization perspective on Winter's simplex volume maximization belief for endmember extraction in hyperspectral remote sensing. Winter's belief, proposed in the late 90's, is very insightful and has led to one of the most widely used class of endmember extraction algorithms nowadays—N-FINDR. Our endeavor to revisit this problem is to provide an alternative,...
Winter's maximum-volume simplex approach is an efficient and representative endmember extraction approach, as evidenced by the fact that N-FINDR, one of the most widely used class of endmember extraction algorithms, employs simplex volume maximization as its criterion. In this work, we consider a robust generalization of Winter's maximum-volume simplex criterion for the noisy scenario. Our development...
In the late 1990s, Winter proposed an endmember extraction belief that has much impact on endmember extraction techniques in hyperspectral remote sensing. The idea is to find a maximum-volume simplex whose vertices are drawn from the pixel vectors. Winter's belief has stimulated much interest, resulting in many different variations of pixel search algorithms, widely known as N-FINDR, being proposed...
Accurate estimation of endmember signatures and the associated abundances of a scene from its hyperspectral observations is at present, a challenging research area. Many of the existing hyper-spectral unmixing algorithms are based on Winter's belief, which states that the vertices of the maximum volume simplex inside the data cloud (observations) will yield high fidelity estimates of the endmember...
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