A Fast N-FINDR Algorithm Based on Cofactor of a Determinant
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摘要: 基于高光譜圖像特征空間幾何分布的端元提取方法通?？煞譃橥队邦愃惴ê蛦涡误w體積最大類算法，通常前者精度不好，后者計(jì)算復(fù)雜度較高。該文提出一種基于代數(shù)余子式的快速N-FINDR端元提取算法(FCA)，該算法融合了投影類算法速度快和單形體體積最大類算法精度高的優(yōu)勢，利用像元投影到端元矩陣元素的代數(shù)余子式構(gòu)成的向量上的方法，尋找最大體積的單形體。此外，該算法在端元搜索方面較為靈活，每次迭代都可用純度更高的像元代替已有端元，因此能保證用該端元確定的單形體，可以將特征空間中全部像元包含在內(nèi)。仿真和實(shí)際高光譜數(shù)據(jù)實(shí)驗(yàn)結(jié)果表明，該文算法在精準(zhǔn)提取出端元的同時(shí)，收斂速度非常快。Abstract: Endmember extraction methods based on geometric?distribution of hyperspectral images usually divide into projection algorithm and the maximum volume formula for simplex, which the former has lower computational complexity and the latter has better precision. A Fast endmember extraction method based on Cofactor of a determinant Algorithm (FCA) is proposed. The algorithm combines the two kinds of algorithms, and which means it has a high speed and accuracy performance for endmember extraction. FCA finds the max volume of simplex by making pixels project to vectors, which are composed of the cofactors of elements in endmember determinant. Besides, FCA is flexible in endmember search, for it can use higher purity pixels to replace the endmembers extracted in the last iteration, which ensures that all the endmembers extracted by FCA are the vertices of simplex. The theoretical analysis and experiments on both simulated and real hyperspectral data demonstrate that the proposed algorithm is a fast and accurate algorithm for endmember extraction.
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Key words:
- Image processing /
- Hyperspectral /
- Endmember extraction /
- Simplex /
- Maximum volume /
- Cofactor /
- Projection
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