Subpixel analysis of Landsat ETM/sup +/ using self-organizing map (SOM) neural networks for urban land cover characterization

This paper examines the subpixel analysis of Landsat ETM/sup +/ data to estimate the percent cover of impervious surface, lawn, and woody tree cover in typical urban/suburban landscapes. By combining Self-Organizing Map (SOM), Learning Vector Quantization (LVQ), and Gaussian Mixture Model (GMM) meth...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2006-06, Vol.44 (6), p.1642-1654
Main Authors: Sangbum Lee, Lathrop, R.G.
Format: Article
Language:English
Subjects:
Classification tree analysis
Ecosystems
Gaussian Mixture Model (GMM)
IKONOS
Independent component analysis
Land surface
landsat
Learning-Vector Quantization (LVQ)
Radiometry
Remote sensing
Satellites
Self-Organizing Map (SOM)
Spatial resolution
subpixel analysis
urban landscape
Vector quantization
Vegetation mapping
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper examines the subpixel analysis of Landsat ETM/sup +/ data to estimate the percent cover of impervious surface, lawn, and woody tree cover in typical urban/suburban landscapes. By combining Self-Organizing Map (SOM), Learning Vector Quantization (LVQ), and Gaussian Mixture Model (GMM) methods, the posterior probability of the various land cover components were estimated for each pixel as a means of subpixel analysis. The estimation of impervious surface and the differentiation of urban vegetation-grass versus woody tree cover-are the main objectives of this paper. Overall, the output estimates compared favorably with those obtained using higher spatial resolution aerial photograph and IKONOS satellite image and traditional hard classification techniques as independent reference. The SOM-LVQ-GMM model showed a moderate degree of similarity in the estimates of impervious surface [root mean-square errors (RMSEs) of
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2006.869984