A neural network classifier for LANDSAT image data

Sei Ichiro Kamata, Richard O. Eason, Arnulfo Perez, Eiji Kawaguchi

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

3 Citations (Scopus)


There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Among the multispectral data, we concentrate on the LANDSAT-5 Thematic Mapper (TM) image data which has been available since 1984-Using the classical maximum likelihood approach, a category is modeled as a multivariate normal distribution; however, the distribution for LANDSAT images is unknown. It is well known that NN approaches have the ability to classify without assuming a distribution. We apply the NN approach to the classification of LANDSAT TM images in order to investigate the robustness of this approach for multi temporal data classification. We confirmed that the NN approach is effective for the classification even if the test data is taken at the different time.

Original languageEnglish
Title of host publicationConference B
Subtitle of host publicationPattern Recognition Methodology and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Print)0818629150
Publication statusPublished - 1992
Externally publishedYes
Event11th IAPR International Conference on Pattern Recognition, IAPR 1992 - The Hague, Netherlands
Duration: 1992 Aug 301992 Sept 3

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651


Other11th IAPR International Conference on Pattern Recognition, IAPR 1992
CityThe Hague

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


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