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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition
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Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition

机译：用于SAR目标识别的斑点 - 噪声不变卷积神经网络

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摘要

Speckle noise is inherent to synthetic aperture radar (SAR) images and degrades the target recognition performance. Deep learning based on convolutional neural networks (CNNs) has been widely applied for SAR target recognition, but the extracted features are still sensitive to speckle noise. In addition, speckle noise has been seldom considered in such CNN-based approaches. In this letter, we propose a speckle-noise-invariant CNN that employs regularization for minimizing feature variations caused by this noise. Before CNN training, we performed SAR image despeckling using the improved Lee sigma filter for feature extraction. Then, we generated SAR images for CNN training by adding speckle noise to the despeckled images. The proposed regularization improves both the feature robustness to speckle noise and SAR target recognition. Experiments on the moving and stationary target acquisition and recognition database demonstrate that the proposed CNN notably improves the classification accuracy compared with the conventional methods.
机译：斑点噪声是合成孔径雷达（SAR）图像固有的，并降低目标识别性能。基于卷积神经网络（CNNS）的深度学习已广泛应用于SAR目标识别，但提取的特征仍然对斑点噪声敏感。此外，基于CNN的方法很少考虑斑点噪声。在这封信中，我们提出了一个散斑 - 噪声不变的CNN，用于最小化由这种噪声引起的特征变化最小化。在CNN培训之前，我们使用改进的Lee Sigma滤波器进行了SAR图像检测，以进行特征提取。然后，我们通过向Dispectled的图像添加散斑噪声来生成用于CNN训练的SAR图像。所提出的正规化改善了特征稳健性与斑点噪声和SAR目标识别。移动和静止目标采集和识别数据库的实验表明，与传统方法相比，所提出的CNN显着提高了分类准确性。

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