Neural net classifier for multi-temporal LANDSAT images using spacial and spectral information

Sei ichiro Kamata, Eiji Kawaguchi

研究成果: Conference contribution

5 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 spatial information. In this paper, we apply the NN approach to the classification of multi-temporal LANDSAT TM images in order to investigate the robustness of two normalization methods using spectral and spatial information. From our experiments, we confirmed that the NN approach with the preprocess is more effective for the classification than the original NN approach even if the test data is taken at the different time.

本文言語English
ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版社Publ by IEEE
ページ2199-2202
ページ数4
ISBN(印刷版)0780314212, 9780780314214
出版ステータスPublished - 1993 12 1
外部発表はい
イベントProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
継続期間: 1993 10 251993 10 29

出版物シリーズ

名前Proceedings of the International Joint Conference on Neural Networks
3

Other

OtherProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3)
CityNagoya, Jpn
Period93/10/2593/10/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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