Hierarchical spectrum recognition is pointed out in the aspect of hyper-spectralimage classification using Hyperion imagesof Lanzhou and Yulin in China， by the comparison study of SVM supervised classification.There are 4 problems for hyper-spectralimage classification: spectral information fidelity image fusion， extraction of sensitive band， removal of "salt and pepper effect" which avoids misclassification for "same body with different spectrum" phenomenon. Hierarchical spectrum recognitionputs forward 4 methods for solving these problems，which are WP-GS image fusion， derivative transformation， object oriented segmentation with 4 scales and multispectral SAM. Hierarchical spectrum recognitioncan identify 9 kinds land type exactly， based on sensitive bands extracted from derivative transformationimage. Visual examination and quantifiable evaluation have been executed to verify authenticity. By contrast， SVM supervised classification is tested， with Gram-Schmidt Spectral sharpening/ Savitzky-Golay convolution filtering/PCA transformation.Using the method of hierarchical spectrum recognition proposed in this paper， the above 4 problems for hyper-spectralimage classification are solved， and good classification results is achieved， with overall classification accuracy and kappa coefficient as 89.52%， 0.852 in Lanzhou， 91.12%、0.873 in Yulin. Growth of 18.68% in Lanzhou and 17.80% in Yulin for overall classification accuracy， as well as17.52% in Lanzhou and 16.89% in Yulin for kappa coefficient are achieved compared with SVM supervised classification.Hierarchical spectrum recognition can provide more accurate recognition results are provided for Hyperion images classification in comparison with SVM supervised classification .