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SAR Targets Classification Based on Deep Memory Convolution Neural Networks and Transfer Parameters.
- Source :
- IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing; Aug2018, Vol. 11 Issue 8, p2834-2846, 13p
- Publication Year :
- 2018
-
Abstract
- Deep learning has obtained state-of-the-art results in a variety of computer vision tasks and has also been used to solve SAR image classification problems. Deep learning algorithms typically require a large amount of training data to achieve high accuracy. In contrast, the size of SAR image datasets is often comparatively limited. Therefore, this paper proposes a novel method, deep memory convolution neural networks (M-Net), to alleviate the problem of overfitting caused by insufficient SAR image samples. Based on the convolutional neural networks (CNN), M-Net adds an information recorder to remember and store samples’ spatial features, and then it uses spatial similarity information of the recorded features to predict unknown sample labels. M-Net's use of this information recorder may cause difficulties for convergence if conventional CNN training methods were directly used to train M-Net. To overcome this problem, we propose a transfer parameter technique to train M-Net in two steps. The first step is to train a CNN, which has the same structure as the part of CNN incorporated in M-Net, to obtain initial training parameters. The second step applies the initialized parameters to M-Net and then trains the entire M-Net. This two-step training approach helps us to overcome the nonconvergence issue, and also reduces training time. We evaluate M-Net using the public benchmark MSTAR dataset, and achieve higher accuracy than several other well-known SAR image classification algorithms. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 19391404
- Volume :
- 11
- Issue :
- 8
- Database :
- Complementary Index
- Journal :
- IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 131487302
- Full Text :
- https://doi.org/10.1109/JSTARS.2018.2836909