Finding optimal neural networks for land use classification

被引：40

作者：

Bischof, H ^{[1
]}

Leonardis, A

机构：

[1] Vienna Tech Univ, Pattern Recognit & Image Proc Grp, A-1040 Vienna, Austria

[2] Univ Ljubljana, Fac Comp & Informat Sci, Ljubljana, Slovenia

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 1998年 / 36卷 / 01期

关键词：

Gaussian maximum likelihood classifier; land use classification; minimum description length (MDL); multilayer perceptron; optimizing neural networks;

D O I：

10.1109/36.655348

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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

引用

页码：337 / 341

页数：5

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