Neural net classifier for multi-temporal LANDSAT TM images

Sei ichiro Kamata*, Eiji Kawaguchi

*この研究の対応する著者

研究成果: Article査読

1 被引用数 (Scopus)

抄録

The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Recently there have been many new developments in neural network (NN) research, and many new applications have been studied. It is well known that NN approaches have the ability to classify without assuming a distribution. We have proposed an NN model to combine the spectral and spacial information of a LANDSAT TM image. In this paper, we apply the NN approach with a normalization method to classify multi-temporal LANDSAT TM images in order to investigate the robustness of our approach. From our experiments, we have confirmed that our approach is more effective for the classification of multi-temporal data than the original NN approach and maximum likelihood approach.

本文言語English
ページ(範囲)1295-1300
ページ数6
ジャーナルIEICE Transactions on Information and Systems
E78-D
10
出版ステータスPublished - 1995 10 1
外部発表はい

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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