Iterative least squares approach to the mixture modeling problem

Author

Srinivasan, Murari ; DeFries, Ruth

Author_Institution

Dept. of Electr. Eng., Maryland Univ., College Park, MD, USA

Volume

4

fYear

1995

fDate

9-12 May 1995

Firstpage

2759

Abstract

The mixture model is an attempt to accurately model the ground truth in the case of low-resolution remote-sensed imagery. The model assumes that a pixel in the image does not consist of a single class, but consists instead of the sum of fractions of various classes. The authors use an iterative least squares approach to estimate these fractions for every pixel. Results are provided on synthetic data as well as real Advanced Very High Resolution Radiometer (AVHRR) data from the African continent

Keywords

geophysical signal processing; image resolution; iterative methods; least squares approximations; parallel algorithms; radiometry; remote sensing; AVHRR data; Advanced Very High Resolution Radiometer; African continent; ground truth; image pixel; iterative least squares; low-resolution remote-sensed imagery; mixture modeling problem; parallel algorithm; synthetic data; Continents; Educational institutions; Geography; High performance computing; Iterative algorithms; Iterative methods; Least squares approximation; Least squares methods; Physics computing; Pixel; Radiometry; Remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on

Conference_Location

Detroit, MI

ISSN

1520-6149

Print_ISBN

0-7803-2431-5

Type

conf

DOI

10.1109/ICASSP.1995.480133

Filename

480133