Hyperspectral data has the features of multi-band

large amounts of data

etc.For hyperspectral remote sensing image classification

traditional methods spend a long operation time in band selection

and image classification accuracy is not high

so first we use differential evolution (DE) for band selection

effectively reduce the redundancy of information and data dimensions

then show the image based on the result of band selection

sample the typical area of the surface feature to identify

finally use gene expression programming (GEP) to bulid classifier for image classification.In band selection

compared with full search algorithm

DE obtains relative good results in a fast time

in GEP image classification

the classification results superior to the traditional KNN algorithm.