Abstract: This paper presented a novel classification algorithm of hyperspectral remote sensing image based on random subspace and kernel extreme learning machine ensemble.Firstly,many feature subsets of the same size are generated from the whole feature of hyperspectral remote sensing image data with random subspace method.Then the base classifiers of kernel extreme learning machine are trained based on these feature subsets.Finally,the classification result is decided by voting strategy.The experimental results on hyperspectral remote sensing image indicate that the proposed method has better performance than the methods based on kernel extreme learning machine and random forest respectively,and has a higher classification overall accuracy.

Key words: Hyperspectral remote sensing image classification,Kernel extreme learning machine,Random subspace,Classifier ensemble