TY - GEN
T1 - A neural network classifier for LANDSAT image data
AU - Kamata, Sei Ichiro
AU - Eason, Richard O.
AU - Perez, Arnulfo
AU - Kawaguchi, Eiji
N1 - Funding Information:
Acknowledgment This work was supported in part by Grant-in-Aid ( No. 02215106 ) for Scientific Research by the Ministry of Education, Science and Culture.
Publisher Copyright:
© 1992 Institute of Electrical and Electronics Engineers Inc. All rights reserved.
PY - 1992
Y1 - 1992
N2 - 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.
AB - 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.
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U2 - 10.1109/ICPR.1992.201843
DO - 10.1109/ICPR.1992.201843
M3 - Conference contribution
AN - SCOPUS:0041312315
SN - 0818629150
T3 - Proceedings - International Conference on Pattern Recognition
SP - 573
EP - 576
BT - Conference B
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IAPR International Conference on Pattern Recognition, IAPR 1992
Y2 - 30 August 1992 through 3 September 1992
ER -