Satellite imagery and exogenous data integration by neural network in automatic land-cover classification

Maria Suelena S. Barros; Maria Conceicao Amorim; Valter Rodrigues

doi:10.1117/12.130866

16 December 1992 Satellite imagery and exogenous data integration by neural network in automatic land-cover classification

Maria Suelena S. Barros, Maria Conceicao Amorim, Valter Rodrigues

Author Affiliations +

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130866
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Maria Suelena S. Barros, Maria Conceicao Amorim, and Valter Rodrigues "Satellite imagery and exogenous data integration by neural network in automatic land-cover classification", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130866

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