Detection of linear objects in ERS-1 SAR images using neural network technology

Author

Hellwich, Olaf

Author_Institution

Chair for Photogrammetry & Remote Sensing, Tech. Univ. Munich, Munchen, Germany

Volume

4

fYear

1994

fDate

8-12 Aug 1994

Firstpage

1886

Abstract

A classification method for the automatic detection of linear objects in synthetic aperture radar (SAR) images is proposed. It is based on feature extraction using a line model, some basic cues from human vision and a neural network classification considering local and global parameters. The method is applied to ERS-1 SAR images to derive the locations of lake and forest boundaries

Keywords

feature extraction; forestry; geophysical signal processing; geophysical techniques; hydrological techniques; image classification; lakes; neural nets; radar applications; radar imaging; remote sensing; remote sensing by radar; spaceborne radar; synthetic aperture radar; ERS-1; SAR image; automatic detection; feature extraction; forest lake; forestry; geophysical measurement technique; hydrology; image classification method; image processing; land surface terrain mapping; line model; linear objects; neural net; neural network; pattern recognition; radar imaging; radar remote sensing; synthetic aperture radar; vegetation; Biological system modeling; Feature extraction; Humans; Image analysis; Image edge detection; Intelligent networks; Neural networks; Object detection; Statistics; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 1994. IGARSS '94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International

Conference_Location

Pasadena, CA

Print_ISBN

0-7803-1497-2

Type

conf

DOI

10.1109/IGARSS.1994.399602

Filename

399602