The present invention is suitable for the image classification, and provides a classification method of the hyperspectral images. The classification method comprises the steps of dividing the hyperspectral images into a training set and a test set, extracting the local feature points, using a K-means algorithm to calculate the local feature points of the training set to form a dictionary, adoptinga KNN algorithm to form the nearest neighbor words for the to-be-classified local feature points of the test set in the dictionary, searching the nearest neighbor feature points for the to-be-classified feature points of the test set images, searching the neighbor word having the shortest spectral dimension distance in the nearest neighbor words, introducing the triple constraints of the neighborfeature point, the neighbor words and the spectral dimension distance, solving a constraint least absolute fitting problem to obtain a coding coefficient, pooling the coding coefficient by a max-pooling algorithm, and classifying the test set by taking the obtained coding coefficient as a feature descriptor of the hyperspectral images. According to the present invention, an indeterminacy problemwhen a mapping relationship of the hyperspectral image feature points and the dictionary words is established is solved,and the discernment of the similar images is improved.