Evolutionary Neural Networks Applied to Land-cover Classification in Zhaoyuan, China

This paper proposes a method for the classification of land cover in remote sensing imagery using evolutionary artificial neural networks (EANN) compared against multilayer perceptrons (MLP) with backpropagation algorithm. Evolutionary neural networks have combined the features of artificial neural networks (ANN) and evolutionary algorithms (EA) in the way that simultaneously evolving ANN architecture and weights. The parsimony of evolved ANN is encouraged by preferring node mutation and connection mutation. This enables consistent reductions of mean square errors of spectral classification with respect to sample pixels. Land-cover classification experiments were carried out by EANN-based classifiers and MLP-based classifiers in a 300times300 pixels Landsat-7 Enhanced Thematic Mapper plus (ETM+) high-resolution image of Zhaoyuan in Shandong province in eastern China. We found that the use of evolutionary algorithms for finding the optimal ANN results mainly in improvements in overall accuracy of an ANN with backpropagation algorithm and produce more compact ANN with good generalization ability in comparison with MLP. It is observed that classification accuracy of up to 90% is achievable for Landsat data produced by EANN.