...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
【24h】

Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net

机译：基于形态学监督的PCA-Net的基于不平衡学习的自动SAR图像变化检测

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is verified on five sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features benefit change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.
机译：由于未更改和已更改的类之间的不平衡，更改检测是一项非常具有挑战性的任务。另外，对数比产生的传统差异图容易出现斑点，从而降低了精度。在这封信中，提出了一种基于PCA网络（PCA-Net）的基于学习的不平衡变化检测，其中设计了一个受监管的PCA-Net，以直接从给定的多时域合成孔径雷达（SAR）图像中获得鲁棒的特征，而不是通过差异图。此外，为了解决变化和不变类之间的不平衡问题，我们提出了一种形态学监督的学习方法，其中利用两个类之间边界附近的像素中的知识来指导网络训练。最后，我们提出的PCA-Net可以通过具有可用参考图的数据集进行训练，并应用于新的数据集，这在变更检测项目中非常实用。我们提出的方法在五组多个时间SAR图像上得到了验证。实验结果表明，利用从边界训练样本的知识，学习到的特征有利于变化检测，并使该方法优于随机抽取样本训练的监督方法。

著录项

  • 来源
    《IEEE Geoscience and Remote Sensing Letters》 |2019年第4期|554-558|共5页
  • 作者

    Wang Rongfang; Zhang Jie; Chen Jiawei; Jiao Licheng; Wang Mi;

  • 作者单位

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

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

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

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

    Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Change detection; imbalance learning; PCA network (PCA-Net); synthetic aperture radar (SAR) images;

    机译：变更检测;不平衡学习;PCA网络（PCA-NET）;合成孔径雷达（SAR）图像;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号