This paper proposed a GPU-based implementation of radiometric normalization algorithms, which is used as a representative case study of on-board data processing techniques for hyperspectral image. Three algorithms of radiometric normalization based on the column average and standard deviation of raw image statistical characteristics were implemented and applied to real hyperspectral images for evaluating their performance. These algorithms have been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C2075 architecture. The airborne Pushbroom Hyperspectral Imager (PHI) was flown to acquire the spectrally contiguous images as experimental datasets. The results show that MN worked best among the three methods and the speedups achieved by the GPU implementation over their CPU counterparts are outstanding.
Military target detection is an important application of hyperspectral remote sensing. It highly demands real-time or near
real-time processing. However, the massive amount of hyperspectral image data seriously limits the processing speed.
Real-time image processing based on hardware platform, such as digital signal processor (DSP), is one of recent
developments in hyperspectral target detection. In hyperspectral target detection algorithms, correlation matrix or
covariance matrix calculation is always used to whiten data, which is a very time-consuming process. In this paper, a
strategy named spatial-spectral information extraction (SSIE) is presented to accelerate the speed of hyperspectral
image processing. The strategy is composed of bands selection and sample covariance matrix estimation. Bands selection
fully utilizes the high-spectral correlation in spectral image, while sample covariance matrix estimation fully utilizes the
high-spatial correlation in remote sensing image. Meanwhile, this strategy is implemented on the hardware platform of
DSP. The hardware implementation of constrained energy minimization (CEM) algorithm is composed of hardware
architecture and software architecture. The hardware architecture contains chips and peripheral interfaces, and software
architecture establishes a data transferring model to accomplish the communication between DSP and PC. In experiments,
the performance on software of ENVI with that on hardware of DSP is compared. Results show that the processing speed
and recognition result on DSP are better than those on ENVI. Detection results demonstrate that the strategy
implemented by DSP is sufficient to enable near real-time supervised target detection.