• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Polarimetric SAR Image Classification Based on Discriminative Dictionary Learning Model
【24h】

Polarimetric SAR Image Classification Based on Discriminative Dictionary Learning Model

机译：基于判别字典学习模型的极化SAR图像分类

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.
机译：极化SAR（PolSAR）图像分类是PolSAR遥感的重要应用之一。这是一个很难解决的高维非线性映射问题，基于学习过完备字典的稀疏表示具有解决此问题的巨大潜力。过完备的字典在PolSAR图像分类中起着重要作用，但是对于PolSAR图像复杂的场景，不同类别共享的特征会削弱学习词典的辨别力，从而降低分类性能。在本文中，我们提出了一种新颖的过完备的字典学习模型，以增强字典的辨别力。所提出的模型所学习的过完备字典更具判别力，非常适合于PolSAR分类。

著录项

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