Proceedings Paper
Satellite imagery and exogenous data integration by neural network in automatic land-cover classification| Format | Member Price | Non-Member Price |
|---|---|---|
| $17.00 | $21.00 |
Paper Abstract
Certainly data integration for land-cover classification requires a non-linear system to associate satellite imagery with exogenous imagery. In this study we present some results of a Neural Network based methodology to provide land-cover classifications. Two approaches are investigated: a) The Monolithic integration: all required registred images are the inputs of only one Back-Error Propagation (BEP) network. The network is trained on purpose to get the final classification. b) The class-distributed integration: for each class a specific network learns from all sattelite imageries its class characteristics. In both approachs, topographic mapping is taken into account as exogenous data.
Paper Details
Date Published: 16 December 1992
PDF: 6 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130866
Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)
PDF: 6 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130866
Show Author Affiliations
Maria Suelena S. Barros, Instituto de Pesquisas Espaciais (Brazil)
Maria Conceicao Amorim, Instituto de Pesquisas Espaciais (Brazil)
Maria Conceicao Amorim, Instituto de Pesquisas Espaciais (Brazil)
Valter Rodrigues, Instituto de Pesquisas Espaciais (United States)
Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)
© SPIE. Terms of Use
