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

Sei ichiro Kamata, Eiji Kawaguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationBetter Understanding of Earth Environment
EditorsSadao Fujimura
PublisherPubl by IEEE
Pages716-718
Number of pages3
ISBN (Print)0780312406
Publication statusPublished - 1993 Dec 1
Externally publishedYes
EventProceedings of the 13th Annual International Geoscience and Remote Sensing Symposium - Tokyo, Jpn
Duration: 1993 Aug 181993 Aug 21

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2

Other

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

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  • Cite this

    Kamata, S. I., & Kawaguchi, E. (1993). Application of neural network approach to classify multi-temporal Landsat images. In S. Fujimura (Ed.), Better Understanding of Earth Environment (pp. 716-718). (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2). Publ by IEEE.