Abstract
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.
Highlights
Hyperspectral remote sensing is a new multi-dimensional information acquisition technology combining image and spectral technologies for monitoring and detecting chemical substances, anomalies, and camouflaged objects, as well as their visual surveillance
We introduce anSSIE strategy to fast near-real-time data processing by taking advantage of high spatial and spectral correlations in hyperspectral image
Multiplication is time-consuming for digital signal processor (DSP), so the spatial-spectral information extraction (SSIE) strategy will reach a high-speed-up ratio compared with no processing strategy
Summary
Hyperspectral remote sensing is a new multi-dimensional information acquisition technology combining image and spectral technologies for monitoring and detecting chemical substances, anomalies, and camouflaged objects, as well as their visual surveillance. The GPU enables real-time execution of the algorithm on a hyperspectral data stream with high spatial and spectral resolution, with acceptable detection performance and a significant margin on computing time [2]. Wang and Chang [8] studied an FPGA implementation of the causal constrained energy minimization (CEM) for hyperspectral target detection, and the experiment showed that the data’s correlation matrix is calculated in a causal manner that only needs data samples up to the sample at the time it is processed. Some other hardware structures are suitable for real-time processing, such as a massively parallel Beowulf cluster [5] Another way is to develop real-time algorithms to improve the execution efficiency of processors. The remainder of this article is organized as follows: the following section introduces target detection algorithm and describes the near-real-time data processing strategy.
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