SAR Target Recognition Using the Multi-aspect-aware Bidirectional LSTM Recurrent Neural Networks

TL;DR

This paper introduces a multi-aspect-aware bidirectional LSTM neural network for SAR target recognition, leveraging space-varying scattering information to significantly improve accuracy and robustness over existing static-image methods.

Contribution

The paper proposes a novel multi-aspect-aware deep learning approach using bidirectional LSTM to incorporate space-varying information in SAR ATR, enhancing recognition performance.

Findings

Achieved 99.9% accuracy on 10-class recognition

Outperformed conventional deep learning methods in noise and confusion tests

Effectively integrated multi-aspect features for improved robustness

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 handle one SAR image, namely learning the static scattering information, while missing the space-varying information. It is obvious that multi-aspect joint recognition introduced space-varying scattering information 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 (TPLBP) are progressively implemented to extract a comprehensive spatial features, followed by dimensionality reduction with the Multi-layer Perceptron (MLP) 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 performance are also better than the conventional deep learning based methods.