遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不...遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不同像元结构和端元混合比例设计了4组样本并测量光谱数据。利用CLSMM计算得到混合像元的反射率,根据均方根误差(root mean square error, RMSE)的变化分析混合度指数和斑块密度指数对分解精度的影响建立混合像元分解误差估算模型并验证模型的精度。结果表明,在一定的实验条件下采用CLSMM计算得到样本的光谱反射数据与实际测量数据的光谱特征基本一致;采用CLSMM的混合像元分解误差与混合度指数、斑块密度指数呈显著的正相关随着2个指数的增加RMSE也呈现明显的上升趋势;利用误差估算模型估算样本的RMSE,发现模型估算的RMSE与原始RMSE相比平均相对误差为16.43%。基于CLSMM进行混合像元分解时,考虑模型的适用场景和像元内部差异性的影响将有利于提高混合像元分解的精度。展开更多
The existing randomized algorithms need an initial estimation of the tubal rank to compute a tensor singular value decomposition.This paper proposes a new randomized fixed-precision algorithm which for a given third-o...The existing randomized algorithms need an initial estimation of the tubal rank to compute a tensor singular value decomposition.This paper proposes a new randomized fixed-precision algorithm which for a given third-order tensor and a prescribed approximation error bound,it automatically finds the tubal rank and corresponding low tubal rank approximation.The algorithm is based on the random projection technique and equipped with the power iteration method for achieving better accuracy.We conduct simulations on synthetic and real-world datasets to show the efficiency and performance of the proposed algorithm.展开更多
基金supported by the National Natural Science Foundation of China (12101559)the Zhejiang Natural Science Foundation (LQ22A010013)+1 种基金the Science Foundation of Zhejiang Sci-Tech University (21062111-Y)the Scientific Research Foundation of Zhejiang Sci-Tech University。
文摘遥感影像中不可避免地包含大量混合像元,传统基于约束线性光谱混合模型(constraint linear spectral mixing model,CLSMM)的混合像元分解往往忽略了像元结构复杂度和端元混合比例的影响。本文采用ASD FieldSpec3高密度反射探头,按照不同像元结构和端元混合比例设计了4组样本并测量光谱数据。利用CLSMM计算得到混合像元的反射率,根据均方根误差(root mean square error, RMSE)的变化分析混合度指数和斑块密度指数对分解精度的影响建立混合像元分解误差估算模型并验证模型的精度。结果表明,在一定的实验条件下采用CLSMM计算得到样本的光谱反射数据与实际测量数据的光谱特征基本一致;采用CLSMM的混合像元分解误差与混合度指数、斑块密度指数呈显著的正相关随着2个指数的增加RMSE也呈现明显的上升趋势;利用误差估算模型估算样本的RMSE,发现模型估算的RMSE与原始RMSE相比平均相对误差为16.43%。基于CLSMM进行混合像元分解时,考虑模型的适用场景和像元内部差异性的影响将有利于提高混合像元分解的精度。
基金the Ministry of Education and Science of the Russian Federation(Grant 075.10.2021.068).
文摘The existing randomized algorithms need an initial estimation of the tubal rank to compute a tensor singular value decomposition.This paper proposes a new randomized fixed-precision algorithm which for a given third-order tensor and a prescribed approximation error bound,it automatically finds the tubal rank and corresponding low tubal rank approximation.The algorithm is based on the random projection technique and equipped with the power iteration method for achieving better accuracy.We conduct simulations on synthetic and real-world datasets to show the efficiency and performance of the proposed algorithm.
