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Nonlinear Aspects of Data Integration for Land-Cover Classification in a NeuralNetwork Environment

机译：神经网络环境下土地覆盖分类数据集成的非线性方面

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摘要

Some results of exploring nonlinear aspects of a neural network methodology toprovide 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 give 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 done to get the final classification in more detailed classes of land use. The performance results show how powerful a neural-network based methodology is for satellite imagery integration and classification.

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