Image segmentation and discriminant analysis for the identification of land cover units in ecology

Author

Lobo, Agustin

Author_Institution

CSIS, Barcelona, Spain

Volume

35

Issue

5

fYear

1997

fDate

9/1/1997 12:00:00 AM

Firstpage

1136

Lastpage

1145

Abstract

The textured nature of most natural land cover units as represented in remotely sensed imagery causes limited results of per-pixel classifications. The segmentation algorithm, iterative mutually optimum region merging (IMORM), is presented and used to partition images into elements that are thereafter classified by linear canonical discriminant analysis and a maximum likelihood allocation rule. This per-segment approach results in much higher accuracy than the conventional per-pixel approach. Furthermore, separability matrices indicate that many land cover categories cannot be correctly defined by per-pixel statistics

Keywords

ecology; geophysical signal processing; geophysical techniques; image classification; image segmentation; image texture; remote sensing; IMORM; algorithm; ecology; image classification; image segmentation; image texture; iterative mutually optimum region merging; land cover unit identification; land surface; linear canonical discriminant analysis; maximum likelihood allocation rule; measurement technique; multispectral method; natural scene; optical imaging; partition; per-segment approach; remote sensing; separability matrices; textured nature; vegetation mapping; Algorithm design and analysis; Environmental factors; Image analysis; Image segmentation; Image texture analysis; Iterative algorithms; Layout; Merging; Partitioning algorithms; Statistics;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.628781

Filename

628781