This paper proposes an automated radiometric normalization method based on Spectral Angle Distance（ SAD） and Euclidean Distance（ ED）. The method is implemented on two hyperspectral images taken by the Compact High Resolution Imaging Spectrometer（ CHRIS） sensor. Experimental results confirm that the proposed method is not only superior to the Mean Absolute Difference（ MAD）-based normalization of radiation characteristics

but also has the advantage of commendably preserving the spectral characteristics.Radiometric normalization minimizes the radiometric differences between two images caused by unstable factors in the acquisition conditions rather than by changes in surface reflectance

which is crucial to land-cover change detection. Radiometric normalization is known as relative radiometric correction. In contrast to absolute correction

the relative method does not need atmospheric data at the moment of image acquisition. This method uses one of the images as reference and then adjusts the radiometric characteristics of the other image

known as subject image

to match the reference image.This paper proposes an automated radiometric normalization method based on SAD and ED. The proposed method selects unchanged pixels by SAD and ED by considering that the same feature has a similar spectrum shape on hyperspectral images.Therefore

we make an attempt to validate the SAD-ED radiometric normalization of multitemporal hyperspectral satellite images.The Mean Absolute Difference（ MAD）-based normalization method is also applied for comparison. In the essay

common evaluation index Root Mean Square Error（ RMSE） and Relative Deviation Index（ RDI） are used to verify the normalized results. Considering the features of hyperspectral remote sensing images

we also apply the spectral fidelity indexes

i. e.

Pearson Correlation Coefficient（ PCC） and spectral distortion degree.The method is implemented on two hyperspectral images taken by the CHRIS sensor. Common evaluation index RMSE and（ RDI） are used to verify the normalized results. PCC and spectral distortion degree are also applied to evaluate spectral fidelity for radiometric normalization of multitemporal hyperspectral satellite imagery. The evaluation results of RMSE and deviation D show that the SAD-ED normalization of CHRIS images is feasible and more effective than the MAD-based normalization. In addition

the evaluation results of PCC and DD show that the SAD-ED normalization performs better in keeping the spectral-dimensional information of hyperspectral images compared with the MAD-based normalization.Experimental results confirm that the proposed method in this essay is not only superior to the MAD-based normalization of radiation characteristics

but also has the advantage of commendably preserving the spectral characteristics of hyperspectral images

thereby having good application prospects.