When the electron plasma is blown from the solar wind and enters into the earth atmosphere, a large number of neutral particles are excited to cause the significant event called aurora phenomenon. In this process, there are several sources of excitation including electron impact, dissociative recombination, thermal electron excitation. Particularly, auroral optical radiation produced by electron impact on oxygen atoms is investigated to explore the relationship between secondary electron energy and spectroscopic emission features. Based on the ground observations of aurora spectral images, the emission characteristics reveal the primary electron energy and flux, the basic atmosphere of species concentration and electron temperature, abundant information of the deposited particles. With the consideration that the radiations of atomic oxygen 5577 Å and 6300 Å are representative auroral spectral lines, we use numerical calculations of relative intensity ratio I(λ5577)/I(λ6300) for various energies to approximate the true ratio. A theoretical primary energy is then determined and used to estimate radiation features at other spectral bands. The best approximated primary characteristic energy is determined as 0.585. The estimated pixel lines of λ6300 and λ6364 underestimate with a factor ranging from 0.95 to 2.2 and from 0.92 to 1.41, respectively.
Aurora has abundant information to be stored. Aurora spectral image electronically preserves spectral information and visual observation of aurora during a period to be studied later. These images are helpful for the research of earth-solar activities and to understand the aurora phenomenon itself. However, the images are produced with a quite high sampling frequency, which leads to the challenging transmission load. In order to solve the problem, lossless compression turns out to be required.
Indeed, each frame of aurora spectral images differs from the classical natural image and also from the frame of hyperspectral image. Existing lossless compression algorithms are not quite applicable. On the other hand, the key of compression is to decorrelate between pixels. We consider exploiting a DPCM-based scheme for the lossless compression because DPCM is effective for decorrelation. Such scheme makes use of two-dimensional redundancy both in the spatial and spectral domain with a relatively low complexity. Besides, we also parallel it for a faster computation speed. All codes are implemented on a structure consists of nested for loops of which the outer and the inner loops are respectively designed for spectral and spatial decorrelation. And the parallel version is represented on CPU platform using different numbers of cores.
Experimental results show that compared to traditional lossless compression methods, the DPCM scheme has great advantage in compression gain and meets the requirement of real-time transmission. Besides, the parallel version has expected computation performance with a high CPU utilization.
Hyperspectral remote sensing technology is widely used in marine remote sensing, geological exploration, atmospheric and environmental remote sensing. Owing to the rapid development of hyperspectral remote sensing technology, resolution of hyperspectral image has got a huge boost. Thus data size of hyperspectral image is becoming larger. In order to reduce their saving and transmission cost, lossless compression for hyperspectral image has become an important research topic. In recent years, large numbers of algorithms have been proposed to reduce the redundancy between different spectra. Among of them, the most classical and expansible algorithm is the Clustered Differential Pulse Code Modulation (CDPCM) algorithm. This algorithm contains three parts: first clusters all spectral lines, then trains linear predictors for each band. Secondly, use these predictors to predict pixels, and get the residual image by subtraction between original image and predicted image. Finally, encode the residual image. However, the process of calculating predictors is timecosting. In order to improve the processing speed, we propose a parallel C-DPCM based on CUDA (Compute Unified Device Architecture) with GPU. Recently, general-purpose computing based on GPUs has been greatly developed. The capacity of GPU improves rapidly by increasing the number of processing units and storage control units. CUDA is a parallel computing platform and programming model created by NVIDIA. It gives developers direct access to the virtual instruction set and memory of the parallel computational elements in GPUs. Our core idea is to achieve the calculation of predictors in parallel. By respectively adopting global memory, shared memory and register memory, we finally get a decent speedup.
Lossless compression algorithms are available for preservation of aurora images. Handling with aurora image compression, linear prediction based method has outstanding compression performance. However, this performance is limited by prediction order and time complexity of linear prediction is relatively high. This paper describes how to solve the conflict between high prediction order and low compression bit rate with an online linear regression with RLS (OLRRLS) algorithm. Experiment results show that OLR-RLS achieves average 7%~11.8% improvement in compression gain and 2.8x speed up in computation time over linear prediction.