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

arXiv: https://arxiv.org/abs/1707.09875 · HTML: https://mlsanity.com/paper/1707.09875/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 2 of 2)

- [Hierarchical Disentanglement-Alignment Network for Robust SAR Vehicle Recognition](https://mlsanity.com/paper/2304.03550/) (2023)
- [Contrastive Learning and Cycle Consistency-based Transductive Transfer Learning for Target Annotation](https://mlsanity.com/paper/2401.12340/) (2024)

## Abstract (arXiv)

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.