吴波, 汪小钦, 张良培. 端元光谱自动提取的总体最小二乘迭代分解[J]. 武汉大学学报 ( 信息科学版), 2008, 33(5): 457-460.
引用本文: 吴波, 汪小钦, 张良培. 端元光谱自动提取的总体最小二乘迭代分解[J]. 武汉大学学报 ( 信息科学版), 2008, 33(5): 457-460.
WU Bo, WANG Xiaoqin, ZHANG Liangpei. Iterative Abstraction of Endmember Based on Total Least Square for Mixture Pixel Decomposition[J]. Geomatics and Information Science of Wuhan University, 2008, 33(5): 457-460.
Citation: WU Bo, WANG Xiaoqin, ZHANG Liangpei. Iterative Abstraction of Endmember Based on Total Least Square for Mixture Pixel Decomposition[J]. Geomatics and Information Science of Wuhan University, 2008, 33(5): 457-460.

端元光谱自动提取的总体最小二乘迭代分解

Iterative Abstraction of Endmember Based on Total Least Square for Mixture Pixel Decomposition

  • 摘要: 根据总体最小二乘(total least squares,TLS)模型理论,提出了一种影像端元光谱可受噪声污染的混合光谱线性扩展模型,并实现了该模型的端元光谱自动迭代提取以及混合像元的限定性分解。实验结果表明,扩展的混合像元分解模型明显优于传统的最小二乘分解模型,总体精度大约提高了10%~20%。

     

    Abstract: An extension linear spectral model based on total least square algorithm is proposed,and iteratively abstract the better endmembers and physically based constrained algorithm with this model are presented. The results show that the proposed model provides better accuracy than traditional least square method,the total estimating precision improves by 10%-20%.

     

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