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Endmember extraction (EE) has been widely utilized to identify spectrally unique signatures of pure ground materials in hyperspectral images. Most of existing EE algorithms focus on spectral signature only, denoted as spectral EE (sEE) algorithms in this paper. In order to improve the performance of these sEE algorithms by considering spatial information, a novel spatial preprocessing (SPP) strategy...
In this paper, we investigate the use of random-projection-based dimensionality reduction for hyperspectral endmember extraction. It is data-independent and computationally more efficient than other widely used dimensionality reduction methods, such as principal component analysis and maximum noise fraction transform. Based on the preliminary result, random-projection-based dimensionality reduction...
Endmember extraction for spectral mixture analysis is a necessary step when endmember information is unknown. If endmembers are assumed to be pure pixels present in an image scene, endmember extraction is to search the most distinctive pixels. Popular algorithms using the criteria of simplex volume maximization (e.g., N-FINDR) and spectral signature similarity (e.g., Vertex Component Analysis) belong...
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