A high-order clustered differential pulse code modulation method with removal of local spectral outliers (C-DPCM-RLSO) is proposed for the lossless compression of hyperspectral images. By adaptively removing the local spectral outliers, the C-DPCM-RLSO method improves the prediction accuracy of the high-order regression predictor and reduces the residuals between the predicted and the original images. The experiment on a set of the NASA Airborne Visible Infrared Imaging Spectrometer (AVIRIS) test images show that the C-DPCM-RLSO method has a comparable average compression gain but a much reduced execution time as compared with the previous lossless methods.

Aurora, the spectacular phenomenon commonly occurring in high latitudes, is caused by the precipitation of energetic particles penetrating the Earth’s atmosphere. Being the result of solar-terrestrial interactions, electron precipitation significantly contributes to auroral production. To evaluate its magnitude, a physical quantity describing the characteristics of precipitating auroral electrons—their characteristic energy—is adopted. In this paper, this quantity is derived from joint data observed by the ground-based auroral spectroscopic imager located in Antarctica Zhongshan Station and the particle detectors “Special Sensor J5 (SSJ5)” on the Defense Meteorological Satellite Program (DMSP) satellites. A postprocessing scheme of ground-based spectral data is proposed to infer the characteristic energy that successively uses classical brute-force, recursive brute-force and self-consistent approximation strategies for step-up speed improvement. Then, the inferred characteristic energies are compared to the average energies calibrated from the relevant electron data detected by SSJ5 to confirm whether this inference is valid. Regarding DMSP F18/SSJ5, these two energy estimations about auroral electrons deviate slightly from each other and show a strong linear relationship. It sheds light on further applications of the valuable aurora spectral data.