A method to classify multispectral images

Author

Zahn, Martin

Author_Institution

Inst. of Photogrammetry & Remote Sensing, Karlsruhe Univ., Germany

Volume

2

fYear

34881

fDate

10-14 Jul1995

Firstpage

1162

Abstract

The author´s method performs an unsupervised clustering of the feature vectors. In order to classify the clusters they are compared with already classified clusters in a database using several metrics. The metrics take into consideration the positions and the shapes of the clusters. The author applies this method to Landsat-TM images to make land-use classifications and significantly better results are obtained than the maximum likelihood classification method

Keywords

geophysical signal processing; geophysical techniques; image classification; optical information processing; remote sensing; Landsat-TM image; feature vector; geophysical measurement technique; land surface; land-use; metrics; multidimensional processing; multispectral image classification; multispectral remote sensing; optical imaging; terrain mapping; unsupervised clustering; visible IR infrared; Clustering algorithms; Hypercubes; Image databases; Maximum likelihood estimation; Multispectral imaging; Pixel; Remote sensing; Satellites; Shape; Spatial databases; Tree graphs;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1995. IGARSS '95. 'Quantitative Remote Sensing for Science and Applications', International

Conference_Location

Firenze

Print_ISBN

0-7803-2567-2

Type

conf

DOI

10.1109/IGARSS.1995.521172

Filename

521172