DocumentCode :
2116953
Title :
The application of artificial neural networks and standard statistical methods to SAR image classification
Author :
Ghinelli, Barbara M G ; Bennett, John C.
Author_Institution :
Dept. of Electron. & Electr. Eng., Sheffield Univ., UK
Volume :
3
fYear :
1997
fDate :
3-8 Aug 1997
Firstpage :
1211
Abstract :
In order to fully utilise SAR techniques, it is important to employ classification schemes which can discriminate between surface cover types having closely related statistics. A hybrid method, consisting of statistical textural measures and radial basis function (RBF) neural networks, is proposed for this problem. Imagery obtained for areas of South American rain forest are employed for this study and standard statistical techniques are used as a benchmark for comparison. A supervised method for training the RBF neural network hidden layer and parameters (e.g. centres, width, etc.) is proposed, based on a minimum-classification-error criterion. This modified RBF network has been applied to the forest data and has been found to outperform standard statistical techniques and the conventional RBF with k-means (or other similar) training method for hidden layer parameters in these classification tasks
Keywords :
feedforward neural nets; forestry; geophysical signal processing; geophysical techniques; geophysics computing; image classification; image texture; radar imaging; remote sensing by radar; statistical analysis; synthetic aperture radar; SAR; South America; artificial neural network; feedforward neural net; forestry; geophysical measurement technique; hidden layer; image classification; image texture; land surface; minimum-classification-error criterion; radar imaging; radar remote sensing; radial basis function; rain forest; statistical method; surface cover type; synthetic aperture radar; terrain mapping; tropical forest; vegetation mapping; Artificial neural networks; Data mining; Earth; Feature extraction; Image resolution; Neural networks; Radial basis function networks; Rivers; Statistical analysis; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International
Print_ISBN :
0-7803-3836-7
Type :
conf
DOI :
10.1109/IGARSS.1997.606400
Filename :
606400
Link To Document :
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