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Combination of GA and ANN to High Accuracy of Polarimetric SAR Data Classification

机译:遗传算法和人工神经网络相结合对极化SAR数据进行高精度分类

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In this paper, a combination of artificial neural network (ANN)and genetic algorithm(GA) has been proposed as a method to obtain a high accuracy in classification of polarimetric SAR data. First we extracted 57 features based on decomposition algorithms and thenthe best features among inputted features by use of GA-ANN wereselected.The classification results of a data set, composed of different land cover elements, exhibited higher accuracy than maximum likelihood and Wishart classifier; moreover the input features were decreased to small numbers which contain sufficient information for classification of data set.
机译:本文提出了一种结合人工神经网络(ANN)和遗传算法(GA)的方法来获得高精度的极化SAR数据分类方法。首先,基于分解算法提取了57个特征,然后利用GA-ANN在输入的特征中选择了最佳特征。由不同的土地覆被元素组成的数据集的分类结果比最大似然法和Wishart分类器具有更高的准确性。此外,输入特征被减少到少量,其中包含足够的信息以用于数据集的分类。

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