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A new spectral unmixing algorithm based on spectral information divergence

机译：一种基于光谱信息发散的新型光谱解密算法

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摘要

Spectral unmixing is a common problem in hyperspectral remote sensing, and it is a key issue of quantitative remote sensing. This article proposed a spectral unmixing algorithm based on spectral information divergence (SID) named SIDSMA. It could improve the precision of abundance estimation through choosing optimal endmember subset used in unmixing. SID-SMA adopted the idea of iteration and added the process of negative endmembers removing which could obviously reduce the computation complexity and improve the speed. Through the results of simulated data from spectral library, it could be seen that the correct proportion of endmember selection by SID-SMA was very high, arriving at 99.86% when the signal-to-noise ratio (SNR) was 100:1. From the point of abundance estimation errors, the algorithm presented here had lower value than two other methods. Especially, when the SNR was 100, the error was less than 0.05.
机译：光谱解密是高光谱遥感中的常见问题，并且是定量遥感的关键问题。本文提出了一种基于名为SIDSMA的光谱信息发散（SID）的光谱解密算法。它可以通过选择解密中使用的最佳终端月子集来提高丰富估计的精度。 SID-SMA采用了迭代的思想，并添加了消减过程的过程，从而消除了，这明显降低了计算复杂性并提高了速度。通过频谱库的模拟数据的结果，可以看出，SID-SMA的正确比例非常高，当信噪比（SNR）为100：1时达到99.86％。从丰富的估计误差点，这里呈现的算法具有比另外两种方法更低的值。特别是，当SNR为100时，误差小于0.05。

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