NONLINEAR ASPECTS OF DATA INTEGRATION FOR LAND-COVER CLASSIFICATION IN A NEURAL-NETWORK ENVIRONMENT

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 sattelite imagery integration and classification.