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Hierarchical Nonlinear Dictionary Learning with Convolutional Neural Networks: Application to Sar Target Recognition | IEEE Conference Publication | IEEE Xplore

Hierarchical Nonlinear Dictionary Learning with Convolutional Neural Networks: Application to Sar Target Recognition


Abstract:

In this paper, a convolutional neural network based hierarchical kernel dictionary learning, which consists of convolutional neural networks (CNN) and dictionary learning...Show More

Abstract:

In this paper, a convolutional neural network based hierarchical kernel dictionary learning, which consists of convolutional neural networks (CNN) and dictionary learning (DL) parts, is proposed for synthetic aperture radar (SAR) target recognition. Compared with conventional DL methods, which use the raw images for training, the CNN part with three convolution layers is utilized to extract the SAR image's hierarchical features. The hierarchical features are introduced into the objective function of DL part. To handle the resulting nonlinear problem, we utilize a nonlinear mapping function to map the dimension-reduced hierarchical features into a higher Hilbert space and perform DL in the space such that the features can be represented linearly. A classification error term is added into the objective function to train a linear classifier. We use the kernel trick to solve the optimization problem. Experiments performed on the MSTAR dataset show that the proposed method outperforms the representative DL methods.
Date of Conference: 11-16 July 2021
Date Added to IEEE Xplore: 12 October 2021
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Conference Location: Brussels, Belgium

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