基于小样本学习的SAR图像识别

深度学习已成为图像识别领域的一个研究热点。与传统图像识别方法不同,深度学习从大量数据中自动学习特征,并且具有强大的自学习能力和高效的特征表达能力。但在小样本条件下,传统的深度学习方法如卷积神经网络难以学习到有效的特征,造成图像识别的准确率较低。因此,提出一种新的小样本条件下的图像识别算法用于解决SAR图像的分类识别。该算法以卷积神经网络为基础,结合自编码器,形成深度卷积自编码网络结构。首先对图像进行预处理,使用2D Gabor滤波增强图像,在此基础上对模型进行训练,最后构建图像分类模型。该算法设计的网络结构能自动学习并提取小样本图像中的有效特征,进而提高识别准确率。在MSTAR数据集的10类目标分类中,选择训练集数据中10%的样本作为新的训练数据,其余数据为验证数据,并且,测试数据在卷积神经网络中的识别准确率为76.38%,而在提出的卷积自编码结构中的识别准确率达到了88.09%。实验结果表明,提出的算法在小样本图像识别中比卷积神经网络模型更加有效。

SAR Image Recognition Based on Few-shot Learning

Deep learning has become a research hotspot in the field of image recognition.Different from traditional image recognition methods,deep learning is to automatically learn features from a large amount data and has a strong ability of feature learning and representation.However,under the condition of small samples,the traditional deep learning methods such as convolutional neural network are difficult to learn effective features,resulting in low image recognition accuracy.Thus,a new image recognition algorithm under small samples was proposed to solve the classification and recognition of SAR images.On the basis of convolutional neural network,it combines convolution operation with autoencoder to form a deep convolutional autoencoder network structure.The algorithm firstly preprocesses the image and enhances the image using 2D Gabor filter,and thentrains the model,finally,constructsthe image classification model.The proposed model can automatically learn and extract effective features from small sample images,and improve the recognition accuracy.On 10 categories of target classification of MSTAR data set,10%samples from the training data were selected as new training data,the rest were valid data,and the recognition accuracy of the test data in the convolutional neural network is 76.38%,while that in the proposed convolutional autoencoder is 88.09%.Experimental results show that the proposed algorithm is more effective than convolutional neural network in small sample image recognition.