The availability of synthetic aperture radar (SAR) images makes practical many geophysical applications. One of them is the classification of terrain structures. In the past years a number of techniques have been developed to classify ground terrain types from SAR images. The conventional approach to classification which assigns a specific class for each region in an image is often inadequate because the area covered by each region may embrace more than a single class. Fuzzy set theory, which has been developed to deal with imprecise information, can provide a more appropriate solution to this problem. In this work has been developed a new supervised fuzzy classification method based on the use of Subsethood Operator. Results of classifying a SAR image are presented and their accuracy is analysed and compared with other standard classification techniques.

The availability of synthetuc aperture radar (SAR) images makes practical may geophysical applications. One of them is the classification of terrain structures. In the past years a number of techniqes hace been developed to classufy ground terrain types from SAR imagnes. Te conventiona approach to classification, which assigns a specific class of each region in an image is often inadequate because the area cvered by each region may embrace more than a single class. Fuzzy set theory theory, which has been developed to deal with imprecise information, can provide a more appropriate solution to this problem. In this work has been developed a new supervised fuzzy classification method based on the use of Subsethood Operator. Results of classifying a SAR image are presented and their accuracy is analysed and compared with other standard classification techniques.

SAR Images Classification Using Fuzzy Subsethood Operator

ANGIULLI, Giovanni;BARRILE, Vincenzo;VERSACI, Mario
2002-01-01

Abstract

The availability of synthetic aperture radar (SAR) images makes practical many geophysical applications. One of them is the classification of terrain structures. In the past years a number of techniques have been developed to classify ground terrain types from SAR images. The conventional approach to classification which assigns a specific class for each region in an image is often inadequate because the area covered by each region may embrace more than a single class. Fuzzy set theory, which has been developed to deal with imprecise information, can provide a more appropriate solution to this problem. In this work has been developed a new supervised fuzzy classification method based on the use of Subsethood Operator. Results of classifying a SAR image are presented and their accuracy is analysed and compared with other standard classification techniques.
2002
1586032801
13: 9781586032807
The availability of synthetuc aperture radar (SAR) images makes practical may geophysical applications. One of them is the classification of terrain structures. In the past years a number of techniqes hace been developed to classufy ground terrain types from SAR imagnes. Te conventiona approach to classification, which assigns a specific class of each region in an image is often inadequate because the area cvered by each region may embrace more than a single class. Fuzzy set theory theory, which has been developed to deal with imprecise information, can provide a more appropriate solution to this problem. In this work has been developed a new supervised fuzzy classification method based on the use of Subsethood Operator. Results of classifying a SAR image are presented and their accuracy is analysed and compared with other standard classification techniques.
Image Classification; Fuzzy Subsethood Operator; Neuro Fuzzy Networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12318/10042
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