The estimation of noise covariance matrix in hyperspectral remotely sensed images

Chien Wen Chen, Hsuan Ren

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Target detection algorithms for hyperspectral remote sensing have been studied for decades. The Least Square (LS) approach is one of the most widely used algorithms. It has been proved that the Noise Whitened Least Square (NWLS) can outperform the original version. But in order to have good results, the estimation of the noise covariance matrix is very important and still remains a great challenge. Many estimation methods have been proposed in the past, including spatial and frequency domain high-pass filter, neighborhood pixel subtraction, etc. In this paper, we further adopt the Fully Constrained Least Square (FCLS), which combine sum-to-one and non-negative constraints, with the NWLS and we also conduct a quantitative comparison with computer simulation of material spectrum from AVIRIS data base on the detection performance and the difference from the designed noise covariance matrix. We will also compare the results with real AVIRIS image scene.

Original languageEnglish
Title of host publicationImaging Spectrometry XI
DOIs
StatePublished - 2006
EventImaging Spectrometry XI - San Diego, CA, United States
Duration: 14 Aug 200616 Aug 2006

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume6302
ISSN (Print)0277-786X

Conference

ConferenceImaging Spectrometry XI
Country/TerritoryUnited States
CitySan Diego, CA
Period14/08/0616/08/06

Keywords

  • Fully Constrained Least Square (FCLS)
  • Hyperspectral
  • Least Square (LS)
  • Noise covariance matrix
  • Noise Whitened Least Square (NWLS)
  • Target detection

Fingerprint

Dive into the research topics of 'The estimation of noise covariance matrix in hyperspectral remotely sensed images'. Together they form a unique fingerprint.

Cite this