首页> 外文会议>Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing >Non-negative matrix factorization pansharpening of hyperspectral data: An application to mid-infrared astronomy
【24h】

Non-negative matrix factorization pansharpening of hyperspectral data: An application to mid-infrared astronomy

机译:高光谱数据的非负矩阵分解泛散,中红外天文学应用

获取原文

摘要

Mid-infrared (wavelengths of 2–25µm) astronomy has progressed significantly in the last decades, thanks to space and ground based telescopes. Space observatories benefit from the absence of atmospheric absorption, allowing to reach the very high sensitivities needed to perform 3D hyperspectral observations at relatively low angular resolution (4”). On the other hand, ground based facilities that suffer from strong atmospheric absorption make use of large (above 8m diameter) telescopes to perform sub-arcsecond resolution imaging through selected windows in the mid-infrared range. In this Paper, we present a method based on non-negative matrix factorization to merge data from space and ground based mid-IR telescopes in order combine the best sensitivity, spectral coverage and angular resolution. We prove the efficiency of this technique on real mid-IR astronomical data, and suggest that it can be applied to any hyper-spectral astronomical data-set.
机译:由于基于空间和地面的望远镜,中红外(波长为2-25μm)天文学在过去几十年中取得了显着的进展。空间观察者受益于没有大气吸收的,允许在相对低的角度分辨率(4“)下执行3D高光谱观测所需的非常高的灵敏度。另一方面,患有强大的大气吸收的地面设施使得使用大(直径超过8M)望远镜来执行通过中红外范围中的所选窗口的子弧形分辨率成像。在本文中,我们提出了一种基于非负矩阵分解的方法,以合并基于空间和地面的中红外望远镜的数据,以便组合最佳灵敏度,光谱覆盖和角度分辨率。我们证明了这种技术在真正的中红外天文数据上的效率,并建议它可以应用于任何超光谱天文数据集。

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