Name
Title | ||
|---|---|---|
Multi-Aspect-Aware Bidirectional LSTM Networks for Synthetic Aperture Radar Target Recognition. |
Abstract | ||
|---|---|---|
The outstanding pattern recognition performance of deep learning brings new vitality to the synthetic aperture radar (SAR) automatic target recognition (ATR). However, there is a limitation in current deep learning based ATR solution that each learning process only handles one SAR image, namely learning the static scattering information, while missing the space-varying information. It is obvious that space-varying scattering information introduced in the multi-aspect joint recognition should improve the classification accuracy and robustness. In this paper, a novel multi-aspect-aware method is proposed to achieve this idea through the bidirectional long short-term memory (LSTM) recurrent neural networks-based space-varying scattering information learning. Specifically, we first select different aspect images to generate the multi-aspect space-varying image sequences. Then, the Gabor filter and three-patch local binary pattern are progressively implemented to extract comprehensive spatial features, followed by dimensionality reduction with the multi-layer perceptron network. Finally, we design a bidirectional LSTM recurrent neural network to learn the multi-aspect features with further integrating the softmax classifier to achieve target recognition. Experimental results demonstrate that the proposed method can achieve 99.9% accuracy for 10-class recognition. Besides, its anti-noise and anti-confusion performances are also better than the conventional deep learning-based methods. |
Year | Venue | Field |
|---|---|---|
2017 | IEEE Access | Dimensionality reduction,Pattern recognition,Automatic target recognition,Synthetic aperture radar,Computer science,Recurrent neural network,Feature extraction,Gabor filter,Artificial intelligence,Deep learning,Perceptron,Distributed computing |
DocType | Volume | Citations |
Journal | 5 | 3 |
PageRank | References | Authors |
0.43 | 26 | 6 |