A multi-temporal classification of multi-spectral images using a neural network

Sei Ichiro Kamata, Michiharu Niimi, Eiji Kawaguchi

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

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 spacial information of LANDSAT TM images. In this paper, we apply the NN approach with a normalization method to classify multi-temporal LANDSAT TM images in order to investigate the robustness of our approach. From our experiments, we confirmed that our approach is more effective for the classification of multi-temporal data than the original NN approach and maximum likelihood approach.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
Subtitle of host publicationPattern Recognition and Neural Networks, ICPR 1994
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages470-472
Number of pages3
ISBN (Electronic)0818662700
Publication statusPublished - 1994
Externally publishedYes
Event12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994 - Jerusalem, Israel
Duration: 1994 Oct 91994 Oct 13

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
Country/TerritoryIsrael
CityJerusalem
Period94/10/994/10/13

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Fingerprint

Dive into the research topics of 'A multi-temporal classification of multi-spectral images using a neural network'. Together they form a unique fingerprint.

Cite this