• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Computer Vision Theory and Applications >Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels
【24h】

Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels

机译：用纯的先前光谱像素解密高光谱图像

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

摘要

In the literature, there are several methods for multilinear source separation. We find the most popular ones such as nonnegative matrix factorization (NMF), canonical polyadic decomposition (PARAFAC). In this paper, we solved the problem of the hyperspectral imaging with NMF algorithm. We based on the physical property to improve and to relate the output endmembers spectra to the physical properties of the input data. To achieve this, we added a regularization which enforces the closeness of the output endmembers to automatically selected reference spectra. Afterwards we accounted for these reference spectra and their locations in the initialization matrices. To illustrate our methods, we used self-acquired hyperspectral images (HSIs). The first scene is compound of leaves at the macroscopic level. In a controlled environment, we extract the spectra of three pigments. The second scene is acquired from an airplane: We distinguish between vegetation, water, and soil.
机译：在文献中，有几种用于多线性源分离的方法。我们发现最受不良矩阵分解（NMF），规范多adic分解（PARAFAC）等最受欢迎的。在本文中，我们解决了NMF算法的高光谱成像问题。我们基于物理性质来改进，并将输出endmemergs光谱与输入数据的物理特性相关。为实现这一目标，我们添加了一个正则化，该正则化，该规则化强制了输出endmembers的亲密度，以自动选择参考光谱。之后，我们考虑了这些参考光谱和它们在初始化矩阵中的位置。为了说明我们的方法，我们使用了自我获得的高光谱图像（HSIS）。第一个场景是宏观水平的叶子化合物。在受控环境中，我们提取三种颜料的光谱。第二场景是从飞机中获得的：我们区分植被，水和土壤。

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