Title :
Detection of linear objects in ERS-1 SAR images using neural network technology
Author_Institution :
Chair for Photogrammetry & Remote Sensing, Tech. Univ. Munich, Munchen, Germany
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;
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
DOI :
10.1109/IGARSS.1994.399602
