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

Sei ichiro Kamata, Eiji Kawaguchi

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

5 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 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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages2199-2202
Number of pages4
ISBN (Print)0780314212, 9780780314214
Publication statusPublished - 1993 Dec 1
EventProceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn
Duration: 1993 Oct 251993 Oct 29

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume3

Other

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

    Fingerprint

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Kamata, S. I., & Kawaguchi, E. (1993). Neural net classifier for multi-temporal LANDSAT images using spacial and spectral information. In Proceedings of the International Joint Conference on Neural Networks (pp. 2199-2202). (Proceedings of the International Joint Conference on Neural Networks; Vol. 3). Publ by IEEE.