On the Evaluation of Different High-Performance Computing Platforms for Hyperspectral Imaging: An OpenCL-Based Approach

Hyperspectral imaging systems are a powerful tool for obtaining surface information in many different spectral channels that can be used in many different applications. Nevertheless, the huge amount of information provided by hyperspectral images also has a downside, since it has to be processed and analyzed. For such purpose, parallel hardware devices, such as field-programmable gate arrays (FPGAs) and graphic processing units (GPUs), are typically used, especially for hyperspectral imaging applications under real-time constraints. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies that can be used to perform the implementation of the desired algorithms in that device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the parallel Fast Algorithm for Linearly Unmixing Hyperspectral Images (pFUN) has been implemented in two different NVIDIA GPUs, the GeForce GTX 980 and the Tesla K40c, using OpenCL. The obtained results are compared with the results provided by the previously developed NVIDIA CUDA implementation of the pFUN algorithm for the same GPU devices for comparing the efficiency of OpenCL against a more specific synthesis design language for the targeted hardware devices, such as CUDA is for NVIDIA GPUs. Moreover, the FUN algorithm has also been implemented into a Bitware Stratix V Altera FPGA, using OpenCL, for comparing the results that can be obtained using OpenCL when targeting different devices and architectures. The obtained results demonstrate the suitability of the followed methodology in the sense that it allows the achievement of efficient FPGA and GPU implementations able to cope with the stringent requirements imposed by hyperspectral imaging systems.

I. Introduction

Hyperspectral imaging systems are considered nowadays as one of the most powerful tools for acquiring surface information in many different fields. These systems are able to provide images in which single pixels have information from across the electromagnetic spectrum of the scene under observation. Whereas the human eye sees color of visible light in mostly three bands (red, green, and blue), hyperspectral imaging systems divide the spectrum into many more contiguous bands, which may be even out of the visible part of the electromagnetic spectrum. The main advantage of this technique is the ability to collect unique “fingerprints” in the electromagnetic spectrum, known as spectral signatures. These “fingerprints” enable identification of the materials that make up a scanned object, providing a much better understanding of the scene under analysis.