A multi-temporal classification of multi-spectral images using a neural network

Sei Ichiro Kamata, Michiharu Niimi, Eiji Kawaguchi

研究成果: Conference contribution

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 LANDSAT TM images. 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 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
ホスト出版物のタイトルProceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
ホスト出版物のサブタイトルPattern Recognition and Neural Networks, ICPR 1994
出版社Institute of Electrical and Electronics Engineers Inc.
ページ470-472
ページ数3
ISBN(電子版)0818662700
出版ステータスPublished - 1994
外部発表はい
イベント12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994 - Jerusalem, Israel
継続期間: 1994 10 91994 10 13

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
2
ISSN(印刷版)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
国/地域Israel
CityJerusalem
Period94/10/994/10/13

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識

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