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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Local Restricted Convolutional Neural Network for Change Detection in Polarimetric SAR Images
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Local Restricted Convolutional Neural Network for Change Detection in Polarimetric SAR Images

机译：局部受限卷积神经网络用于极化SAR图像变化检测

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摘要

To detect changed areas in multitemporal polarimetric synthetic aperture radar (SAR) images, this paper presents a novel version of convolutional neural network (CNN), which is named local restricted CNN (LRCNN). CNN with only convolutional layers is employed for change detection first, and then LRCNN is formed by imposing a spatial constraint called local restriction on the output layer of CNN. In the training of CNN/LRCNN, the polarimetric property of SAR image is fully used instead of manual labeled pixels. As a preparation, a similarity measure for polarimetric SAR data is proposed, and several layered difference images (LDIs) of polarimetric SAR images are produced. Next, the LDIs are transformed into discriminative enhanced LDIs (DELDIs). CNN/LRCNN is trained to model these DELDIs by a regression pretraining, and then a classification fine-tuning is conducted with some pseudolabeled pixels obtained from DELDIs. Finally, the change detection result showing changed areas is directly generated from the output of the trained CNN/LRCNN. The relation of LRCNN to the traditional way for change detection is also discussed to illustrate our method from an overall point of view. Tested on one simulated data set and two real data sets, the effectiveness of LRCNN is certified and it outperforms various traditional algorithms. In fact, the experimental results demonstrate that the proposed LRCNN for change detection not only recognizes different types of changed/unchanged data, but also ensures noise insensitivity without losing details in changed areas.
机译：为了检测多时相极化合成孔径雷达（SAR）图像中的变化区域，本文提出了一种新版本的卷积神经网络（CNN），称为局部受限CNN（LRCNN）。首先使用仅具有卷积层的CNN进行变化检测，然后通过在CNN的输出层上施加称为局部约束的空间约束来形成LRCNN。在CNN / LRCNN的训练中，完全利用了SAR图像的偏振特性，代替了人工标记的像素。作为准备，提出了极化SAR数据的相似性度量，并生成了极化SAR图像的多个分层差异图像（LDI）。接下来，将LDI转换为可区分的增强型LDI（DELDI）。 CNN / LRCNN被训练为通过回归预训练对这些DELDI建模，然后使用从DELDI获得的一些伪标记像素进行分类微调。最后，从训练后的CNN / LRCNN的输出直接生成显示变化区域的变化检测结果。还讨论了LRCNN与传统的变化检测方法之间的关系，以从整体角度说明我们的方法。通过对一个模拟数据集和两个真实数据集进行测试，LRCNN的有效性得到了验证，其性能优于各种传统算法。实际上，实验结果表明，提出的用于变化检测的LRCNN不仅可以识别不同类型的变化/未变化数据，而且可以确保噪声不敏感，而不会丢失变化区域的细节。

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  • 作者

    Liu Fang; Jiao Licheng; Tang Xu; Yang Shuyuan; Ma Wenping; Hou Biao;

  • 作者单位

    Nanjing Univ Sci & Technol, Minist Educ, Key Lab Intelligent Percept & Syst High Dimens In, Nanjing 210094, Jiangsu, Peoples R China|Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

    Xidian Univ, Joint Int Res Lab Intelligent Percept & Computat, Key Lab Intelligent Percept & Image Understanding, Sch Artificial Intelligence,Minist Educ,Int Res C, Xian 710071, Shaanxi, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Change detection; convolutional neural network (CNN); local restricted CNN (LRCNN); polarimetric synthetic aperture radar (SAR) image;

    机译：变化检测;卷积神经网络（CNN）;局部受限CNN（LRCNN）;极化合成孔径雷达（SAR）图像;

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