Neural network ensemble based on rough sets reduction is proposed to decrease the computing complexity of conventional feature ensemble selection algorithms. Firstly

a dynamic reduction technology

which integrates genetic algorithm and resample method

is used to get reduct sets that have stable and good generalization ability. Secondly

Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble

the best generalization ability neural network ensemble can be found by some search strategies. Finally

classification based on neural network ensemble can be completed by combination with vote rule. The method has been verified in the experiment of classifying Landsat 7 bands remote sensing image of chosen area. A number of feature sets of poor performance were discarded by reduction based on rough sets. Compared to conventional feature selection algorithms

the method takes less time

has lower computing complexity

and the performance is satisfying.