基于深度学习的SAR特征提取与目标识别研究
发布时间：2018-12-16 22:21
【摘要】：SAR图像的应用需求与日俱增,SAR图像目标识别技术也在发展。由于硬件性能提升和有效训练算法的提出,近年深度学习重获关注,并在图像识别领域取得成功。本文应用深度学习的理论和方法,结合SAR图像的特点,研究了基于深度学习的SAR特征提取与目标识别方法。主要研究内容如下:根据SAR图像的特性指出了SAR图像目标识别中的难点。SAR图像目标具有多变不确定的特点,传统识别方法需要大量的专业知识,需要对图像预处理,不能自动提取有效的特征。深度学习具有盲学习和无监督学习的能力,本文使用深度学习解决该问题,将普通神经网络、深度置信网络和卷积神经网络三种深度结构分别用于三类和十类的SAR目标识别。通过对比发现在带标签样本足够的情况下,深度置信网络的预训练对普通神经网络提升不大,二者的识别性能几乎相同。深度学习对参数和结构十分敏感,实验发现卷积神经网络在不同激活函数下对SAR目标识别结果差异巨大,其中ReLu函数最适合作为卷积神经网络的激活函数。接下来分析了卷积神经网络中间结构对SAR目标识别的影响。池化层分别选择mean-pooling和max-pooling,对比识别结果,并利用池化后的特征重构图像,对比与原图像的相似度,结果表明mean-pooling更适合作为SAR目标识别时的池化层特征选择方法。改变卷积核大小发现最适合的卷积核大小和目标图像尺寸是相关的。本文还考虑了SAR图像目标在有遮挡情况下的识别问题。卷积神经网络在目标区域50%遮挡率的情况下识别率有所下降。Dropout方法的思想在于每次训练时随机丢弃部分神经元,这样训练出来的模型具备了只使用部分信息进行推断预测的能力。实验结果证明了在SAR目标遮挡的情况下使用了Dropout的神经网络识别率有所提高。三种深度模型的对比显示卷积神经网络对SAR目标特征提取的可分性好于其他二者,对三类和十类目标的识别率分别达到了99.8%和96.3%,明显高于前两种模型。对卷积神经网络提取的特征可视化,发现卷积神经网络在特征提取上能很好地抓住SAR图像的局部相关性,保持图像的空间结构。
[Abstract]:The application demand of SAR image is increasing day by day, and the target recognition technology of SAR image is also developing. Due to the improvement of hardware performance and the development of effective training algorithm, the depth learning has been paid more attention in recent years, and it has been successful in the field of image recognition. Based on the theory and method of depth learning and the characteristics of SAR image, this paper studies the method of SAR feature extraction and target recognition based on depth learning. The main research contents are as follows: according to the characteristics of SAR image, the difficulties in SAR image target recognition are pointed out. SAR image target has the characteristics of changeable uncertainty, traditional recognition methods need a lot of professional knowledge, and need to preprocess the image. Can not automatically extract valid features. Depth learning has the ability of blind learning and unsupervised learning. In this paper, we use deep learning to solve this problem, and apply three depth structures of general neural network, depth confidence network and convolutional neural network to SAR target recognition of three and ten categories, respectively. It is found by comparison that the pre-training of the depth confidence neural network has little effect on the ordinary neural networks and their recognition performance is almost the same when the labeled samples are sufficient. Deep learning is very sensitive to parameters and structures. It is found that the results of SAR target recognition by convolutional neural networks vary greatly under different activation functions, and ReLu function is the most suitable for the activation functions of convolutional neural networks. Then the effect of convolution neural network intermediate structure on SAR target recognition is analyzed. The results of mean-pooling and max-pooling, contrast recognition are selected in the pool layer, and the reconstructed images are reconstructed by using the pooled features. The results show that mean-pooling is more suitable to be used as the feature selection method for SAR target recognition. Changing the size of the convolutional kernel, the most suitable size of the convolution kernel is found to be related to the size of the target image. The problem of target recognition in SAR images with occlusion is also considered in this paper. The recognition rate of convolutional neural network decreases with 50% occlusion rate in the target region. The idea of Dropout method is to discard some neurons at random during each training. This trained model has the ability to infer and predict only a portion of the information. The experimental results show that the recognition rate of neural network using Dropout is improved when SAR targets are occluded. The comparison of the three depth models shows that the convolution neural network has better separability than the other two models, and the recognition rates of the three and ten kinds of targets are 99.8% and 96.3%, respectively, which are significantly higher than those of the former two models. By visualizing the feature extracted by convolution neural network, it is found that convolution neural network can grasp the local correlation of SAR image and keep the spatial structure of the image.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.52
本文编号：2383136
[Abstract]:The application demand of SAR image is increasing day by day, and the target recognition technology of SAR image is also developing. Due to the improvement of hardware performance and the development of effective training algorithm, the depth learning has been paid more attention in recent years, and it has been successful in the field of image recognition. Based on the theory and method of depth learning and the characteristics of SAR image, this paper studies the method of SAR feature extraction and target recognition based on depth learning. The main research contents are as follows: according to the characteristics of SAR image, the difficulties in SAR image target recognition are pointed out. SAR image target has the characteristics of changeable uncertainty, traditional recognition methods need a lot of professional knowledge, and need to preprocess the image. Can not automatically extract valid features. Depth learning has the ability of blind learning and unsupervised learning. In this paper, we use deep learning to solve this problem, and apply three depth structures of general neural network, depth confidence network and convolutional neural network to SAR target recognition of three and ten categories, respectively. It is found by comparison that the pre-training of the depth confidence neural network has little effect on the ordinary neural networks and their recognition performance is almost the same when the labeled samples are sufficient. Deep learning is very sensitive to parameters and structures. It is found that the results of SAR target recognition by convolutional neural networks vary greatly under different activation functions, and ReLu function is the most suitable for the activation functions of convolutional neural networks. Then the effect of convolution neural network intermediate structure on SAR target recognition is analyzed. The results of mean-pooling and max-pooling, contrast recognition are selected in the pool layer, and the reconstructed images are reconstructed by using the pooled features. The results show that mean-pooling is more suitable to be used as the feature selection method for SAR target recognition. Changing the size of the convolutional kernel, the most suitable size of the convolution kernel is found to be related to the size of the target image. The problem of target recognition in SAR images with occlusion is also considered in this paper. The recognition rate of convolutional neural network decreases with 50% occlusion rate in the target region. The idea of Dropout method is to discard some neurons at random during each training. This trained model has the ability to infer and predict only a portion of the information. The experimental results show that the recognition rate of neural network using Dropout is improved when SAR targets are occluded. The comparison of the three depth models shows that the convolution neural network has better separability than the other two models, and the recognition rates of the three and ten kinds of targets are 99.8% and 96.3%, respectively, which are significantly higher than those of the former two models. By visualizing the feature extracted by convolution neural network, it is found that convolution neural network can grasp the local correlation of SAR image and keep the spatial structure of the image.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN957.52
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