Radiometric normalization is a key pre-processing technology for thematic mapping using hyperspectral images

which aims at eliminating response differences among the detectors. The short wave and infrared bands of the Tiangong-1 hyperspectral images were radiometric normalized using column average and standard deviation methods. Thus

three radiometric normalization algorithms including mean normalization

moment matching normalization and adjacent column balanced normalization were used for performance evaluation. Meanwhile

these three algorithms were parallel implemented on graphics processing units and compute device unified architecture. The parallel implementation methods mainly by decomposition the processing flow

which the CPU focus on procedure control and the GPU focus on data level parallel computing. A mapping model also established between the parallel computing units and the image pixels for further performance improvement. Overall

the parallel computing methods achieved a speedup about 5 times to 7 times when compared with the CPU counterparts. The proposed radiometric normalization algorithms dependent on image statistics and easy for parallel computing

which provides a thoughtful perspective on the potentials of adapting these techniques to on-board as well as on-the-ground hyperspectral image real time processing.