Comparison of scene segmentations: SMAP, ECHO, and maximum likelihood

Author

McCauley, James Darrell ; Engel, Bernard A.

Author_Institution

Dept. of Agric. Eng., Purdue Univ., West Lafayette, IN, USA

Volume

33

Issue

6

fYear

1995

fDate

11/1/1995 12:00:00 AM

Firstpage

1313

Lastpage

1316

Abstract

Sequential maximum a posteriori (SMAP) and the extraction and classification of homogeneous objects (ECHO), two spectral/spatial scene segmentation algorithms, were compared with traditional maximum likelihood (ML) estimation in a supervised classification of multispectral data. SMAP generalized better than both ECHO and ML. Significant differences were found in all mean class classification accuracies: SMAP>ECHO>ML

Keywords

geophysical signal processing; geophysical techniques; image classification; image segmentation; infrared imaging; maximum likelihood estimation; optical information processing; remote sensing; ECHO; IR imaging; SMAP; extraction and classification of homogeneous objects; geophysical measurement technique; image classification; image processing; image segmentation; land surface; maximum likelihood; multispectral method; optical imaging; scene segmentation; sequential maximum a posteriori; spatial scene segmentation algorithm; spectral segmentation algorithm; supervised classification; terrain mapping; visible; Bayesian methods; Data mining; Image segmentation; Layout; Markov random fields; Maximum a posteriori estimation; Maximum likelihood estimation; Pixel; Recursive estimation; Testing;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.477185

Filename

477185