Finding Optimal Neural Networks for Land Use Classification

Horst Bischof; Ales Leonardis

doi:10.1109/36.655348

Finding Optimal Neural Networks for Land Use Classification

Horst Bischof, Ales Leonardis

Institut für Maschinelles Sehen und Darstellen (7100)

Publikation: Beitrag in einer Fachzeitschrift › Artikel › Begutachtung

Abstract

The authors present a fully automatic and computationally efficient algorithm based on the minimum description length principle (MDL) for optimizing multilayer perceptron (MLP) classifiers. They demonstrate their method on the problem of multispectral Landsat image classification. They compare their results with a hand-designed MLP and a Gaussian maximum likelihood classifier, in which their method produces better classification accuracy with a smaller number of hidden units.

Originalsprache	englisch
Seiten (von - bis)	337-341
Fachzeitschrift	IEEE Transactions on Geoscience and Remote Sensing
Jahrgang	36
Ausgabenummer	1
DOIs	https://doi.org/10.1109/36.655348
Publikationsstatus	Veröffentlicht - 1998

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

Zugriff auf Dokument

10.1109/36.655348

Dieses zitieren

@article{8b5ae95ce94146f1ad63aee86b8e0bf4,

title = "Finding Optimal Neural Networks for Land Use Classification",

abstract = "The authors present a fully automatic and computationally efficient algorithm based on the minimum description length principle (MDL) for optimizing multilayer perceptron (MLP) classifiers. They demonstrate their method on the problem of multispectral Landsat image classification. They compare their results with a hand-designed MLP and a Gaussian maximum likelihood classifier, in which their method produces better classification accuracy with a smaller number of hidden units.",

author = "Horst Bischof and Ales Leonardis",

year = "1998",

doi = "10.1109/36.655348",

language = "English",

volume = "36",

pages = "337--341",

journal = "IEEE Transactions on Geoscience and Remote Sensing",

issn = "1558-0644",

publisher = "Institute of Electrical and Electronics Engineers",

number = "1",

}

TY - JOUR

T1 - Finding Optimal Neural Networks for Land Use Classification

AU - Bischof, Horst

AU - Leonardis, Ales

PY - 1998

Y1 - 1998

N2 - The authors present a fully automatic and computationally efficient algorithm based on the minimum description length principle (MDL) for optimizing multilayer perceptron (MLP) classifiers. They demonstrate their method on the problem of multispectral Landsat image classification. They compare their results with a hand-designed MLP and a Gaussian maximum likelihood classifier, in which their method produces better classification accuracy with a smaller number of hidden units.

AB - The authors present a fully automatic and computationally efficient algorithm based on the minimum description length principle (MDL) for optimizing multilayer perceptron (MLP) classifiers. They demonstrate their method on the problem of multispectral Landsat image classification. They compare their results with a hand-designed MLP and a Gaussian maximum likelihood classifier, in which their method produces better classification accuracy with a smaller number of hidden units.

U2 - 10.1109/36.655348

DO - 10.1109/36.655348

M3 - Article

SN - 1558-0644

VL - 36

SP - 337

EP - 341

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

IS - 1

ER -

Finding Optimal Neural Networks for Land Use Classification

Abstract

UN SDGs

Zugriff auf Dokument

Fingerprint

Dieses zitieren