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A method for blind separation of components information from mixed pixel

机译:一种从混合像素中盲分离成分信息的方法

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摘要

In the field of remote sensing, it is important to separate the component information from mixed pixel. If the physical process of remote sensing can be expressed by a set of linear equations, the remote sensing information matrix is equal to the weight matrix multiplied by the component information matrix. Generally speaking, the precondition of retrieval of component information matrix is that the weight matrix is known. However, the blind signal separation (BSS) method can separate the matrix unconditionally, whose basic principle is that the additive information needing separation can be achieved from the statistical characteristics contained in a mass of samples in the remotely sensed information matrix. Therefore, the values of the component information matrix and the weight matrix can be estimated. The wave shape of components can be retrieved by BSS, but the amplitude cannot. In this paper, the plant-soil mixed pixels were chosen as the studying targets in this paper to quantitatively separate the component information and solve the uncertainty of BSS. Simulation and field test verify the reliability of the method. Results show that the BSS can be one of the effective methods of mixed pixel separation, and the foreground of application is very promising.
机译:在遥感领域,重要的是将成分信息与混合像素分开。如果遥感的物理过程可以由一组线性方程式表示,则遥感信息矩阵等于权重矩阵乘以分量信息矩阵。一般而言,检索组成信息矩阵的前提是权重矩阵是已知的。然而,盲信号分离(BSS)方法可以无条件地分离矩阵,其基本原理是可以从遥感信息矩阵中大量样本所包含的统计特征中获得需要分离的附加信息。因此,可以估计成分信息矩阵和权重矩阵的值。分量的波形可以通过BSS检索,但幅度不能。本文以植物-土壤混合像素为研究对象,定量分离组分信息,解决了BSS的不确定性。仿真和现场测试验证了该方法的可靠性。结果表明,BSS可以作为一种有效的混合像素分离方法,其应用前景十分广阔。

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