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Recently, real-time image data processing is a popular research area for hyperspectral remote sensing. In particular, target detection surveillance, which is an important military application of hyperspectral remote sensing, demands real-time or near real-time processing. The massive amount of hyperspectral image data seriously limits the processing speed. In this article, a strategy named spatial-spectral information extraction (SSIE) is presented to accelerate hyperspectral image processing. SSIE is composed of band selection and sample covariance matrix estimation. Band 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. To overcome the inconsistent and irreproducible shortage of random distribution, we present an effective scalar method to select sample pixels. Meanwhile, we have implemented this target detection algorithm based on the SSIE strategy on the hardware of a digital signal processor (DSP). The implementation of a constrained energy minimization algorithm is composed of hardware and software architectures. The hardware architecture contains chips and peripheral interfaces, while software architecture contains a data transferring model. In the experiments, we compared the performance of hardware of DSP with that of Environment for Visualizing Images software. DSP speed up the data processing and also results in more effective in terms of recognition rate, which demonstrate that the SSIE implemented by DSP is sufficient to enable near real-time supervised target detection. © 2012 Zhang et al.; licensee Springer.

Real-time target detection in hyperspectral images based on spatial-spectral information extraction