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Convolutional Neural Network-Based Dictionary Learning for SAR Target Recognition
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DOI：10.1109/LGRS.2020.3008212.png)
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摘要
En 中文
In this letter, a novel convolutional neural network (CNN)-based dictionary learning (DL) method is proposed for synthetic aperture radar (SAR) target recognition. Different from conventional target recognition schemes, which consist of the hand-crafted feature extraction followed by a classifier, the proposed scheme utilizes a well-designed ConvNet as the feature extractor, and it can automatically learn hierarchies of features from the training data set. The outputs of the ConvNet are regarded as multifeature and are used for the following multi-DL. For a classification task, we take into consideration the mean-squared error (MSE) combined with a regularization term as the loss function. As a result, the whole architecture combines the ConvNet and DL as an end-to-end framework. We show the back propagation of the loss and update the variables using the stochastic gradient descent with the momentum method. Experiments performed on the moving and stationary target automatic recognition (MSTAR) data set exhibit that the proposed method outperforms many state-of-the-art DL and CNN methods in terms of recognition performance.
Keyword:
Feature extraction
Synthetic aperture radar
Target recognition
Dictionaries
Task analysis
Convolution
Image recognition
Convolutional neural network (CNN)
dictionary learning (DL)
synthetic aperture radar (SAR) target recognition
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