Application of neural network approach to classify multi-temporal Landsat images

Sei ichiro Kamata*, Eiji Kawaguchi

*この研究の対応する著者

研究成果: Conference contribution

2 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 use the spectral and spacial 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 a normalization method. From our experiments, we confirmed that the NN approach with the preprocessing 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Better Understanding of Earth Environment
編集者Sadao Fujimura
出版社Publ by IEEE
ページ716-718
ページ数3
ISBN(印刷版)0780312406
出版ステータスPublished - 1993 12 1
外部発表はい
イベントProceedings of the 13th Annual International Geoscience and Remote Sensing Symposium - Tokyo, Jpn
継続期間: 1993 8 181993 8 21

出版物シリーズ

名前International Geoscience and Remote Sensing Symposium (IGARSS)
2

Other

OtherProceedings of the 13th Annual International Geoscience and Remote Sensing Symposium
CityTokyo, Jpn
Period93/8/1893/8/21

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 地球惑星科学(全般)

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