您当前的位置：
首页 >
文章列表页 >
Spatial-spectral reweighted sparse multi-layer nonnegative matrix factorization for hyperspectral image unmixing
Information Sciences | 更新时间：2025-02-20
|

    • Spatial-spectral reweighted sparse multi-layer nonnegative matrix factorization for hyperspectral image unmixing

    • “In the field of hyperspectral remote sensing image unmixing, researchers have proposed a new sparse multi-layer non negative matrix factorization algorithm with spatial spectral reweighting, which effectively reduces the influence of noise and improves the unmixing performance.”
    • Optics and Precision Engineering   Vol. 32, Issue 22, Pages: 3348-3365(2024)

    • DOI：10.37188/OPE.20243222.3348    

      CLC： TP394.1;TH691.9

    • Received：11 June 2024，

      Revised：08 August 2024，

      Published：25 November 2024

    移动端阅览

  • TANG Jiming,BAO Wenxing,LEI Bingbing,et al.Spatial-spectral reweighted sparse multi-layer nonnegative matrix factorization for hyperspectral image unmixing[J].Optics and Precision Engineering,2024,32(22):3348-3365. DOI： 10.37188/OPE.20243222.3348.

  •  
  •  

0

Views

10

下载量

0

CSCD

Alert me when the article has been cited
提交
Tools
Download
Export Citation
Share
Add to favorites
Add to my album

Related Articles

Film flattening system in space cameras

Related Author

TAO Jia-Sheng

Related Institution

Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences
0