基于多层自编码器的SAR目标和阴影共享表示

Q2 Physics and Astronomy
Zhi-jun Sun, Lei Xue, Yang-ming Xu, Zhijun Sun
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引用次数: 1

摘要

研究了合成孔径雷达(SAR)图像的自动目标识别(ATR)问题。提出了一种基于多层自编码器的SAR特征提取算法。该方法利用概率神经网络和受限玻尔兹曼机(RBM)对环境的概率分布进行建模。深度学习模型通过形成更具表现力的多层神经网络,学习目标的共享表示及其反映目标形状特征的阴影轮廓。通过两种识别模型对目标进行自动分类。基于MSTAR的实验结果验证了该算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shared Representation of SAR Target and Shadow Based on Multilayer Auto-encoder
Automatic Target Recognition (ATR) of Synthetic Aperture Radar (SAR) images is investigated. A SAR feature extraction algorithm based on a multilayer auto-encoder is proposed. The method makes use of a probabilistic neural network and Restricted Boltzmann Machine (RBM) modeling probability distribution of the environment. Through the formation of a more expressive multilayer neural network, the deep learning model learns the shared representation of the target and its shadow outline reflecting the target shape characteristics. Targets are classified automatically through two recognition models. The experiment results based on the MSTAR verify the effectiveness of the proposed algorithm.
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来源期刊
雷达学报
雷达学报 Physics and Astronomy-Instrumentation
CiteScore
4.10
自引率
0.00%
发文量
882
期刊介绍: Information not localized
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