Application of ReliefF algorithm to selecting feature sets for classification of high resolution remote sensing image

In classification, a large number of features often make it difficult to select appropriate classification features. In such situations, feature selection or dimensionality reduction methods play an important role in classification. ReliefF algorithm is one of the most successful filtering feature selection methods. In this paper, some shortcomings of the ReliefF algorithm are improved, on the problem of poor stability of neighbor samples selection, proposing the method of using the average value of multiple random selection to improve the anti-volatility of the algorithm. And redundant analysis is added to the ReliefF algorithm to eliminate the redundant features. The experimental results show that the improved ReliefF algorithm can effectively establish the classification feature sets, achieve the better classification accuracy.