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Titolo:
CLASSIFICATION OF REMOTELY-SENSED DATA BY AN ARTIFICIAL NEURAL-NETWORK - ISSUES RELATED TO TRAINING DATA CHARACTERISTICS
Autore:
FOODY GM; MCCULLOCH MB; YATES WB;
Indirizzi:
UNIV COLL SWANSEA,DEPT MATH & COMP SCI SWANSEA SA2 8PP W GLAM WALES UNIV COLL SWANSEA,DEPT GEOG SWANSEA SA2 8PP W GLAM WALES
Titolo Testata:
Photogrammetric engineering and remote sensing
fascicolo: 4, volume: 61, anno: 1995,
pagine: 391 - 401
Fonte:
ISI
Lingua:
ENG
Soggetto:
MAXIMUM-LIKELIHOOD CLASSIFICATION; SUPERVISED CLASSIFICATION; CLOUD CLASSIFICATION; MEMBERSHIP; ACCURACY; EXAMPLE; SETS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Science Citation Index Expanded
Citazioni:
42
Recensione:
Indirizzi per estratti:
Citazione:
G.M. Foody et al., "CLASSIFICATION OF REMOTELY-SENSED DATA BY AN ARTIFICIAL NEURAL-NETWORK - ISSUES RELATED TO TRAINING DATA CHARACTERISTICS", Photogrammetric engineering and remote sensing, 61(4), 1995, pp. 391-401

Abstract

Artificial neural networks have considerable potential for the classification of remotely sensed data. In this paper a feed-forward artificial neural network using a variant of the back-propagation learning algorithm was used to classify agricultural crops from synthetic aperture radar data. The performance of the classification, in terms of classification accuracy, was assessed relative to a conventional statistical classifier, a discriminant analysis. Classifications of training data sets showed that the artificial neural network appears able to characterize classes better than the discriminant analysis, with accuraciesof up to 98 percent observed. This better characterization of the training data need not, however, translate into a significantly more accurate classification of an independent testing set. The results of a series of classifications are presented which show that in general markedly higher classification accuracies may be obtained from the artificial neural network, except when a priori information on class occurrence is incorporated into the discriminant analysis, when the classification performance was similar to that of the artificial neural network. These and other issues were analyzed further with reference to classifications of synthetic data sets. The results illustrate the dependencyof the two classification techniques on representative training samples and normally distributed data.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 23/09/20 alle ore 23:32:58