| 特征选择与深度学习相结合的极化SAR图像分类 |
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| 引用本文： | 韩萍,孙丹丹.特征选择与深度学习相结合的极化SAR图像分类[J].信号处理,2019,35(6):972-978. |
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| 作者姓名： | 韩萍 孙丹丹 |
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| 作者单位： | 中国民航大学智能信号与图像处理天津市重点实验室 |
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| 基金项目： | 国家自然科学基金项目(61571442);国家重点研发计划(2016YFB0502405) |
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| 摘 要： | 给出了一种特征选择与深度学习相结合的极化合成孔径雷达(polarimetric synthetic aperture radar, PolSAR)图像有监督分类算法。该算法首先根据极化SAR图像数据以及目标分解获取原始特征参数集，然后利用随机森林(Random Forest, RF)方法对特征参数集进行重要性评估，并根据特征重要性排名选择最优极化特征。以最优极，化特征为输入，通过卷积神经网络(convolutional neural network, CNN)学习多层特征信息，再利用训练好的网络模型对极化SAR图像进行分类。利用美国AIRSAR机载系统采集的实测数据进行实验,并同已有经典有监督分类算法进行比较，结果表明本文算法能够选取有效的极化特征，最终得到较为准确的分类效果。
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| 关 键 词： | 特征选择 深度学习 极化合成孔径雷达 随机森林 卷积神经网络 有监督分类 |
| 收稿时间： | 2019-01-02 |
Classification of Polarimetric SAR Image with Feature Selection and Deep Learning |
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| Affiliation: | Tianjin Key Lab for Advanced Signal Processing, CAUC |
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| Abstract: | A supervised classification algorithm with feature selection and deep learning for polarimetric synthetic aperture radar (PolSAR) image is proposed in this paper. Firstly, an original feature parameter set is extracted from the polarization SAR image data and the target decomposition. Then the random forest method is used to evaluate the importance of the feature parameter set. After that, the optimal polarization features are obtained according to the feature importance rank.Taking the optimal polarization feature as the input, the multi-layer feature information is learned by the convolutional neural network (CNN), and the PolSAR image is classified by the trained network model. Experiments are carried out using the measured data collected by the U.S. AIRSAR airborne system, and the results are compared with which of the existing classical supervised classification algorithm. The results show that the proposed algorithm can select effective polarization features and finally obtain more accurate classification results. |
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