Using spectral-shape parameters to improve linear spectral mixture analysis

Linear spectral mixture analysis (LSMA) has been frequently used to derive sub-pixel information from moderate-resolution satellite images. This letter proposes a new method to improve LSMA using spectral-shape parameters. A Mann-Whitney U test, Wilcoxon W test statistical analysis and root mean squ...

Full description

Saved in:
Bibliographic Details
Published inInternational journal of remote sensing Vol. 30; no. 22; pp. 6061 - 6067
Main Authors Chen, Fang, Tang, Junmei
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 01.01.2009
Subjects
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
Internal geophysics
Landscapes
Mathematical models
Satellites
Spectra
Spectral lines
Statistical analysis
Uranium
landscapes
models
Space remote sensing
data processing
Satellite observation
accuracy
vegetation
utilization
Information
new methods
imagery
Parameter
Ratio
statistical analysis
soils
standard deviation
Online AccessGet full text

Cover

More Information
Summary:Linear spectral mixture analysis (LSMA) has been frequently used to derive sub-pixel information from moderate-resolution satellite images. This letter proposes a new method to improve LSMA using spectral-shape parameters. A Mann-Whitney U test, Wilcoxon W test statistical analysis and root mean square error (RMSE) were used to compare the fractions estimated from satellite images using traditional LSMA with a 'shade' endmember (LSMAWS), the normalized spectral mixture by mean ratio (NSMMR), the proposed spectral-shape-based LSMA (SSLSMA) and the 'actual' fractions generated from an ortho-image quarter quadrangle. These statistical analyses suggest that the accuracy was significantly improved using the spectral-shape-based LSMA model in identifying landscape classes at the sub-pixel level.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Article-2
ObjectType-Feature-1
ISSN:0143-1161
1366-5901
DOI:10.1080/01431160902950871