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 language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Place of Publication | Piscataway, NJ, United States |
| Publisher | Publ by IEEE |
| Pages | 2199-2202 |
| Number of pages | 4 |
| Volume | 3 |
| ISBN (Print) | 0780314212, 9780780314214 |
| Publication status | Published - 1993 |
| Externally published | Yes |
| Event | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) - Nagoya, Jpn Duration: 1993 Oct 25 → 1993 Oct 29 |
Other
| Other | Proceedings of 1993 International Joint Conference on Neural Networks. Part 1 (of 3) |
|---|---|
| City | Nagoya, Jpn |
| Period | 93/10/25 → 93/10/29 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Neural net classifier for multi-temporal LANDSAT images using spacial and spectral information. / Kamata, Seiichiro; Kawaguchi, Eiji.
Proceedings of the International Joint Conference on Neural Networks. Vol. 3 Piscataway, NJ, United States : Publ by IEEE, 1993. p. 2199-2202.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural net classifier for multi-temporal LANDSAT images using spacial and spectral information
AU - Kamata, Seiichiro
AU - Kawaguchi, Eiji
PY - 1993
Y1 - 1993
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0027797757&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027797757&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0027797757
SN - 0780314212
SN - 9780780314214
VL - 3
SP - 2199
EP - 2202
BT - Proceedings of the International Joint Conference on Neural Networks
PB - Publ by IEEE
CY - Piscataway, NJ, United States
ER -