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Endmember extraction is an important task for hyperspectral analysis; the accurate identification of endmembers enables efficient spectral unmixing and classification. In the paper, a new endmember extraction algorithm based on a modified MSS approach with LP error as initial spectrum selection algorithm, and OPD measure as similarity is proposed. The endmembers extracted by modified MSS are more...
Endmember extraction is an important step in spectral mixture analysis when endmembers are unknown. Endmembers are usually assumed to be pure pixels present in an image scene. Under this circumstance, endmember extraction is to find the most distinctive pixels. To make the searching process more efficient, the sequential forward search (SFS) method is generally used, where the next endmember is determined...
Most of sequential endmember extraction algorithms, such as iterative error analysis (IEA), vertex component analysis (VCA), and simplex growing algorithm (SGA), use sequential forward selection (SFS) searching strategy. The advantage is its low computational complexity. However, it is sensitive to the initial condition. To reduce the “nesting effect”, sequential forward floating selection (SFFS)...
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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