In this paper, the results of the segmentation process of polarimetric multiband SAR images are shown. Purpose of the work is the image interpretation in absence of ground-truth. The segmentation process is performed by the Self Organizing Map network which is an unsupervised neural network. The objective of the segmentation is the selection of homogeneous regions on the image and the results are evaluated in terms of grey level statistics on same restricted areas (urban and salina areas).

SIR-C polarimetric image segmentation by neural network

Satalino G;Pasquariello;
1996

Abstract

In this paper, the results of the segmentation process of polarimetric multiband SAR images are shown. Purpose of the work is the image interpretation in absence of ground-truth. The segmentation process is performed by the Self Organizing Map network which is an unsupervised neural network. The objective of the segmentation is the selection of homogeneous regions on the image and the results are evaluated in terms of grey level statistics on same restricted areas (urban and salina areas).
1996
Istituto di Studi sui Sistemi Intelligenti per l'Automazione - ISSIA - Sede Bari
0-7803-3068-4
Algorithms
Image segmentation
Image understanding
Moisture
Neural networks
Radar measurement
Sensors
Soils
Surface roughness
Synthetic aperture radar
Grey level statistics
Image interpretation
Polarimetric image segmentation
Salina area
Self organizing map network
Urban area
Radar imaging
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/217848
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