Classification of urban areas in multi-date ERS-1 images using structural features and a neural network

Author

Hagg, Wilhelm ; Segl, Karl ; Sties, Manfred

Author_Institution

Inst. for Photogammetry & Remote Sensing, Karlsruhe Univ., Germany

Volume

2

fYear

34881

fDate

10-14 Jul1995

Firstpage

901

Abstract

Describes a new method to extract structural informations from images. The loss of spatial resolution and distortions-from edges, as it occurs with standard texture algorithms, are reduced to a minimum. Furthermore, the authors describe the inhomogeneity by three different structure types according to the structures contained in SAR images. Finally they use a neural network (RBF-Network) to get a more precise classification of urban areas from SAR images

Keywords

feature extraction; geophysical signal processing; geophysical techniques; image classification; image sequences; image texture; neural nets; radar applications; radar imaging; remote sensing by radar; spaceborne radar; synthetic aperture radar; RBF-Network; SAR image; feature extraction; geophysical measurement technique; image classification; image processing; image sequence; inhomogeneity; land surface; multi-date ERS-1 image; neural net; neural network; radar remote sensing; structural feature; terrain mapping; urban area; Area measurement; Data mining; Distortion measurement; Feature extraction; Image resolution; Loss measurement; Neural networks; Radial basis function networks; Spatial resolution; Urban areas;

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.1995.521091

Filename

521091