Knowledge-based segmentation of Landsat images

Author

Ton, Jezching ; Sticklen, Jon ; Jain, Anil K.

Author_Institution

Dept. of Comput. Sci., Michigan State Univ., East Lansing, MI, USA

Volume

29

Issue

2

fYear

1991

fDate

3/1/1991 12:00:00 AM

Firstpage

222

Lastpage

232

Abstract

A knowledge-based approach for Landsat image segmentation is proposed. The image segmentation problem is solved by extracting kernel information from the input image to provide an initial interpretation of the image and by using a knowledge-based hierarchical classifier to discriminate between major land-cover types in the study area. The proposed method is designed in such a way that a Landsat image can be segmented and interpreted without any prior image-dependent information. The general spectral land-cover knowledge is constructed from the training land-cover data, and the road information of an image is obtained through a road-detection program

Keywords

computerised pattern recognition; computerised picture processing; geophysical techniques; geophysics computing; knowledge based systems; remote sensing; Landsat; computer method; geophysical technique; hierarchical classifier; image segmentation; kernel information; knowledge-based approach; land surface remote sensing; land-cover types; road-detection program; Computer science; Data mining; Expert systems; Image generation; Image segmentation; Kernel; Reflectivity; Remote sensing; Satellites; Training data;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/36.73663

Filename

73663