Abstract: High-resolution remote sensing image can provide rich feature details．However,a variety of terrain has complex spatial distribution,and spectral heterogeneity of similar landcovers appears largely,which bring great challenge to traditional pattern recognition classifier．For this purpose,this paper put forward a novel multi-classifier combination method for remote sensing image classification based on adaptive weights adjustment for different query samples．Previous multiple features combination classifiers fail to make full use of local correlation among them,with a unifying weight for all the samples．This paper explored different weights of each feature in classification on different test samples,according to different local distributions．The experimental results on a large remote sensing image database show that different features in remote sensing image classification of different samples have different effects,and the sample-specific multiple features weighting-based method presented in this paper enhances the average classification accuracy from 78.3% to 90%.

Key words: Remote sensing image classification,Adaptive weighting,Features combination,Multiple classifiers