Radiometric correction of hyperspectral imaging data in spacial dimension and spectral dimension

Because sample are affected by light, lens and sensor in optical channel in hyperspectral imaging system, image quality and spectrums are also affected. The paper proposes the gray correction coefficient algorithm with spatial dimension and spectral dimension to preprocess molecular hyperspectral imaging data. The experimental results show that the algorithm can carry out radiometric correction in spatial dimension and spectral dimension and eliminate the effects of light, lens and sensor. Image quality is significantly improved. The spectrum curves indicating true biochemical characteristics of the sample are extracted that classification results based on spectral information are better than uncorrected sample.