Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition

Huang, Xiayuan; Qiao, Yu; Qiao, Hong

doi:10.1109/lgrs.2020.2983718

Cited by 28 publications

(19 citation statements)

References 19 publications

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“…In the remote sensing field, Zhang et al [48] used MobileNetV2 as the backbone and introduced channel attention to extract deep features and improve performance in HSRRS image classification. Huang et al [49] used light-CNN to extract features from the remote sensing image and obtained good accuracy and an effective network scale and parameters.…”

Section: B Depthwise Separable Convolutionmentioning

confidence: 99%

CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism

Wang

Yuan

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Compared with natural scenes, aerial scenes are usually composed of numerous objects densely distributed within the aerial view, and thus more key local semantic features are needed to describe them. However, when existing CNNs are used for remote sensing image classification, they typically focus on the global semantic features of the image, and especially for deep models, shallow and intermediate features are easily lost. This paper proposes a channel-spatial attention mechanism based on a depthwise separable convolution (CSDS) network for aerial scene classification to solve these challenges. First, we construct a depthwise separable convolution (DS-Conv) and pyramid residual connection architecture.DS-Conv extracts features from each channel and merges them, effectively reducing the number of necessary calculations, and the pyramid residual connections connect the features from multiple layers and create associations. Then, the channel-spatial attention algorithm causes the model to obtain more effective features in the channel and spatial domains. Finally, an improved cross-entropy loss function is used to reduce the impact of similar categories on backpropagation. Comparative experiments on three public data sets show that the CSDS network can achieve results comparable to those of other state-of-the-art methods. In addition, visualization of feature extraction results by the Grad-CAM algorithm and ablation experiments for each module reflect the powerful feature learning and representation capabilities of the proposed CSDS network.

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Section: B Depthwise Separable Convolutionmentioning

confidence: 99%

CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism

Wang

Yuan

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…EOCs consist of three experiments: depression angle variant, configuration variant, and noise corruption. Unlike some papers which only verify the performance of the network under SOC [19,36], to evaluate the generalization performance of the proposed ASIR-Net, this paper also measure the recognition accuracy rates of the proposed ASIR-Net under EOCs.…”

Section: Dataset Descriptionmentioning

confidence: 99%

A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Zhou

et al. 2021

Remote Sensing

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Automatic target recognition (ATR) in synthetic aperture radar (SAR) images has been widely used in civilian and military fields. Traditional model-based methods and template matching methods do not work well under extended operating conditions (EOCs), such as depression angle variant, configuration variant, and noise corruption. To improve the recognition performance, methods based on convolutional neural networks (CNN) have been introduced to solve such problems and have shown outstanding performance. However, most of these methods rely on continuously increasing the width and depth of networks. This adds a large number of parameters and computational overhead, which is not conducive to deployment on edge devices. To solve these problems, a novel lightweight fully convolutional neural network based on Channel-Attention mechanism, Channel-Shuffle mechanism, and Inverted-Residual block, namely the ASIR-Net, is proposed in this paper. Specifically, we deploy Inverted-Residual blocks to extract features in high-dimensional space with fewer parameters and design a Channel-Attention mechanism to distribute different weights to different channels. Then, in order to increase the exchange of information between channels, we introduce the Channel-Shuffle mechanism into the Inverted-Residual block. Finally, to alleviate the matter of the scarcity of SAR images and strengthen the generalization performance of the network, four approaches of data augmentation are proposed. The effect and generalization performance of the proposed ASIR-Net have been proved by a lot of experiments under both SOC and EOCs on the MSTAR dataset. The experimental results indicate that ASIR-Net achieves higher recognition accuracy rates under both SOC and EOCs, which is better than the existing excellent ATR methods.

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“…BN and DP are the abbreviations of BatchNorm and Dropout. C is not shown in Figure 2b, which is simply constructed according to (13). The total number of parameters in CVAE-GAN is about 33.46 million.…”

Section: Network Architecturementioning

confidence: 99%

“…Different from the traditional technologies which extract the image features manually, the deep learning (DL) technology can automatically extract the image features by combining the feature extractor and the classifier, so the remarkable performance on target recognition can be achieved. Several results on some public data sets for SAR-ATR by using DL (deep learning) have been reported and are far beyond the results by using traditional technologies [11][12][13]. However, there are some obvious limitations on the DL-based SAR-ATR studies at present.…”

Section: Introductionmentioning

confidence: 99%

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

Feng

Guo

et al. 2021

Remote Sensing

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Even though deep learning (DL) has achieved excellent results on some public data sets for synthetic aperture radar (SAR) automatic target recognition(ATR), several problems exist at present. One is the lack of transparency and interpretability for most of the existing DL networks. Another is the neglect of unknown target classes which are often present in practice. To solve the above problems, a deep generation as well as recognition model is derived based on Conditional Variational Auto-encoder (CVAE) and Generative Adversarial Network (GAN). A feature space for SAR-ATR is built based on the proposed CVAE-GAN model. By using the feature space, clear SAR images can be generated with given class labels and observation angles. Besides, the feature of the SAR image is continuous in the feature space and can represent some attributes of the target. Furthermore, it is possible to classify the known classes and reject the unknown target classes by using the feature space. Experiments on the MSTAR data set validate the advantages of the proposed method.

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Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition

Cited by 28 publications

References 19 publications

CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism

CSDS: End-to-End Aerial Scenes Classification With Depthwise Separable Convolution and an Attention Mechanism

A Lightweight Fully Convolutional Neural Network for SAR Automatic Target Recognition

Feature Learning for SAR Target Recognition with Unknown Classes by Using CVAE-GAN

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