On radar polarization target decomposition theorems with application to target classification, by using neural network method
On radar polarization target decomposition theorems with application to target classification, by using neural network method
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发表时间：
1991-04
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通讯作者：
E. Pottier;J. Saillard
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作者：
E. Pottier;J. Saillard
第一部分回顾了雷达全息术和高分辨率雷达成像的基本原理，给出了典型雷达目标的实际测量结果。然后在第二部分引入了雷达极化目标分解定理的概念，将米勒矩阵分解成与单个平均目标和剩余分量相关的两部分。在最后部分，他们介绍了一种新的基于神经网络建模的极化分类方法，并展示了如何从四个高分辨率电磁雷达图像的知识中识别散射体。>；
In the first part the authors recall the basic principles of 'radar holography' and high resolution electromagnetic radar imaging construction, and present a real measurement of canonical radar targets. Then they introduce in the second part, the concept of 'radar polarization target decomposition theorems' to separate the Mueller matrix in two parts which are related to a single averaged target and to a residue component. In the last part, they introduce a new polarimetric method of classification based on a neural network modelization, and show how it is possible to identify a scatterer from the knowledge of the four high resolution electromagnetic radar images. >