Nonlinear aspects of data integration for land-cover classification in a neural network environment

Details Nonlinear aspects of data integration for land-cover classification in a neural network environment Nonlinear aspects of data integration for land-cover classification in a neural network environment

Mar 1994

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1994adspr..14..265s&link_type=abstract

Advances in Space Research, Volume 14, Issue 3, p. 265-268.

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Some results concerning the exploration of a neural network methodology's nonlinear aspects to provide land-cover in satellite imagery are presented. All required images are used in a Back-Error Propagation (BEP) network which is a nonlinear data integrator for spatial patterns classification. The network is trained to recognize the basic categories: grass, moisted soil, bare soil, forest, water and built-up areas. The results of a partial classification are used in a posterior analysis which is made to get the final classification in more detailed classes of land use. The performance results show how powerful is a neural-network based methodology for satellite imagery integration and classification.

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