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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Distribution and Structure Match Generative Adversarial Network for SAR Image Classification
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A Distribution and Structure Match Generative Adversarial Network for SAR Image Classification

机译：SAR图像分类的分布与结构匹配生成对抗网络

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摘要

Synthetic aperture radar (SAR) image classification is a fundamental research in the interpretation of SAR images. The previous methods are unilaterally based on statistical features or spatial features, which cannot capture features with complete SAR image characteristics and unavoidably limits the performance for classification. In this article, novel sample weighting and class adversarial training strategies are proposed to fuse complementary SAR characteristics. Based on these, a distribution and structure match auxiliary classifier generative adversarial network (DSM-ACGAN) is constructed for high-quality discriminative feature learning. Particularly, the characteristics of statistical distribution and spatial structure are jointly considered in class adversarial training of DSM-ACGAN. On the one hand, DSM-ACGAN sets the true SAR image characteristics as goals for the generator to learn generative models of each category. On the other hand, and more importantly, it guides the discriminator to simultaneously capture the desired statistical and structural features. Through the class adversarial processing, the discriminative feature learning progressively improves and contributes to classification. Additionally, class-balanced and plausible samples can be generated. Experimental results on three broad SAR images from different satellites confirm the effectiveness of class adversarial training and the superiority of discriminative feature learning in DSM-ACGAN. Visual performance and quantitative metrics also show the state-of-the-art performance of the novel model.
机译：合成孔径雷达（SAR）图像分类是SAR图像解释的基本研究。以前的方法基于统计特征或空间特征，不能捕获具有完整的SAR图像特征的特征，并且不可避免地限制分类的性能。在本文中，提出了新的样品加权和阶级的对抗培训策略，融合了互补特征。基于这些，构建了分布和结构匹配辅助分类器发生的对抗网络（DSM-accag）以用于高质量的鉴别特征学习。特别地，统计分布和空间结构的特征是在DSM-accan的课堂敌对训练中共同考虑的。一方面，DSM-ACGAN将真实的SAR图像特征作为生成器学习每个类别的生成模型的目标。另一方面，更重要的是，它引导鉴别器同时捕获所需的统计和结构特征。通过阶级对抗性加工，歧视特征学习逐步改善并有助于分类。此外，可以生成类平衡和合理的样本。不同卫星的三种广义图像的实验结果证实了DSM-acgar中课堂对抗训练的有效性和鉴别特征学习的优越性。视觉性能和定量度量还显示了新型模型的最先进的性能。

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  • 作者

    Ren Zhongle; Hou Biao; Wu Qian; Wen Zaidao; Jiao Licheng;

  • 作者单位

    Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China;

    Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China;

    Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China;

    Northwestern Polytech Univ Key Lab Informat Fus Technol Minist Educ Xian 710072 Peoples R China;

    Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Int Res Ctr Intelligent Percept & Computat Xian 710071 Peoples R China|Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Xian 710071 Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Discriminative feature learning; generative adversarial network (GAN); image classification; synthetic aperture radar (SAR);

    机译：歧视特征学习;生成的对抗网络（GaN）;图像分类;合成孔径雷达（SAR）;

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