Abstract
Due to the large number of Sigmoid activation function derivation in the traditional convolution neural network (CNN), it is difficult to solve the question of the low efficiency of extracting the feature of Synthetic Aperture Radar (SAR) images. The Sigmoid activation function in the CNN is improved to be a rectified linear unit (ReLU) activation function, and the classifier is modified by the Extreme Learning Machine (ELM). Finally, in this CNN model, the improved CNN works as the feature extractor and ELM performs as a recognizer. A SAR image recognition algorithm based on the CNN-ELM algorithm is proposed by combining the CNN and the ELM algorithm. The experiment is conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database which contains 10 kinds of target images. The experiment result shows that the algorithm can realize the sparsity of the network, alleviate the overfitting problem, and speed up the convergence speed of the network. It is worth mentioning that the running time of this experiment is very short. Compared with other experiment on the same database, it indicates that this experiment has generated a higher recognition rate. The accuracy of the SAR image recognition is 100%.
Highlights
Synthetic Aperture Radar (SAR) is an important mean to obtain information, and it is widely used in geological survey, topographic mapping, utilization of marine resources, and so on
Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database show that the classifier based on principal component analysis (PCA) is better than the bias classifier based on Gaussian model under the condition of limited training data [1]
In order to test the performance of the proposed CNNELM algorithm for SAR image recognition, comparisons are made with principal component analysis (PCA) [1], deep convolutional networks (DCN) [4], sparse representation classification (SRC) [2], complementary spatial pyramid coding (CSPC) [3], combination convolutional nets and support vector machine (CN-SVM) [25], and convolution neural network (CNN)-SVM [7] methods, respectively
Summary
Synthetic Aperture Radar (SAR) is an important mean to obtain information, and it is widely used in geological survey, topographic mapping, utilization of marine resources, and so on. Wang et al proposed a complementary spatial pyramid coding (CSPC) approach in the framework of spatial pyramid matching Both the coding coefficients and coding residuals are explored to develop more discriminative and robust features for representing SAR images [3]. Cho and Park proposed CNN architecture using aggregated features and fully connected layers, which the accuracy recognizing the 10 classes of military targets on MSTAR dataset is 94.38% [24]. In 2006, Huang and LeCun combined convolutional networks (CN) and SVM algorithm to identify targets and generated a high recognition rate [25]. A new SAR image recognition method based on the CNN-ELM algorithm is proposed in this paper. The method is characterized by a high recognition rate and short running time
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