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Endmember extraction based on modified Iterative Error Analysis

机译:基于修正迭代误差分析的端元提取

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Iterative Error Analysis (IEA) widely known as a good endmember extraction (EE) algorithm. It is robust, automatic and free of data transformation. However, IEA is faced with risks in some cases due to the sole use of unmxing distance, and its speed is lowed down by the iteration-based linear spectral mixture analysis (LSMA). To make IEA algorithm faster and more robust, its modified version is proposed based on two substitutions. One is substituting integrated distance for unmxing distance, which makes the algorithm more robust. The other is substituting SVM-based multiple endmember spectral mixture analysis (MESMA) for iteration-based LSMA, which speeds up the algorithm greatly. Experiments show that the modified IEA algorithm outperforms original one in terms of both robustness and running speed.
机译:迭代错误分析(IEA)被广泛称为良好的端成员提取(EE)算法。它功能强大,自动且无数据转换。但是,在某些情况下,由于仅使用取消混合距离会导致IEA面临风险,并且基于迭代的线性光谱混合分析(LSMA)会降低IEA的速度。为了使IEA算法更快,更健壮,在两个替代的基础上提出了其改进版本。一种是用积分距离代替非混合距离,这使算法更加健壮。另一种方法是将基于SVM的多端成员光谱混合分析(MESMA)替换为基于迭代的LSMA,这大大加快了算法的速度。实验表明,改进后的IEA算法在鲁棒性和运行速度方面均优于原始算法。

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