...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International journal of remote sensing >A variational method for multisource remote-sensing image fusion
【24h】

A variational method for multisource remote-sensing image fusion

机译：一种多源遥感图像融合的变分方法

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

With the increasing availability of multisource image data from Earth observation satellites, image fusion, a technique that produces a single image which preserves major salient features from a set of different inputs, has become an important tool in the field of remote sensing since usually the complete information cannot be obtained by a single sensor. In this article, we develop a new pixel-based variational model for image fusion using gradient features. The basic assumption is that the fused image should have a gradient that is close to the most salient gradient in the multisource inputs. Meanwhile, we integrate the inputs with the average quadratic local dispersion measure for the purpose of uniform and natural perception. Furthermore, we introduce a split Bregman algorithm to implement the proposed functional more effectively. To verify the effect of the proposed method, we visually and quantitatively compare it with the conventional image fusion schemes, such as the Laplacian pyramid, morphological pyramid, and geometry-based enhancement fusion methods. The results demonstrate the effectiveness and stability of the proposed method in terms of the related fusion evaluation benchmarks. In particular, the computation efficiency of the proposed method compared with other variational methods also shows that our method is remarkable.
机译：随着来自地球观测卫星的多源图像数据可用性的提高，图像融合（一种产生单个图像并保留来自一组不同输入的主要显着特征的技术）已成为遥感领域的重要工具，因为通常单个传感器无法获取信息。在本文中，我们开发了一种新的基于像素的变体模型，用于使用梯度特征进行图像融合。基本假设是，融合图像的梯度应接近多源输入中最明显的梯度。同时，为了统一和自然感知，我们将输入与平均二次局部色散度量相结合。此外，我们引入了分裂的Bregman算法来更有效地实现所提出的功能。为了验证该方法的效果，我们在视觉上和定量上将其与传统的图像融合方案进行了比较，例如拉普拉斯金字塔，形态金字塔和基于几何的增强融合方法。结果证明了该方法在相关融合评估基准方面的有效性和稳定性。特别地，与其他变分方法相比，所提方法的计算效率也表明我们的方法是杰出的。

著录项

  • 来源
    《International journal of remote sensing》 |2013年第8期|2470-2486|共17页
  • 作者

    Faming Fang; Fang Li; Guixu Zhang; Chaomin Shen;

  • 作者单位

    Department of Computer Science, East China Normal University, Shanghai, China;

    Department of Mathematics, East China Normal University, Shanghai, China;

    Department of Computer Science, East China Normal University, Shanghai, China;

    Department of Computer Science, East China Normal University, Shanghai, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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