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FPGA implementation of the principal component analysis algorithm for dimensionality reduction of hyperspectral images

机译：高光谱图像降维的主成分分析算法的FPGA实现

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Remotely sensed hyperspectral imaging is a very active research area, with numerous contributions in the recent scientific literature. The analysis of these images represents an extremely complex procedure from a computational point of view, mainly due to the high dimensionality of the data and the inherent complexity of the state-of-the-art algorithms for processing hyperspectral images. This computational cost represents a significant disadvantage in applications that require real-time response, such as fire tracing, prevention and monitoring of natural disasters, chemical spills, and other environmental pollution. Many of these algorithms consider, as one of their fundamental stages to fully process a hyperspectral image, a dimensionality reduction in order to remove noise and redundant information in the hyperspectral images under analysis. Therefore, it is possible to significantly reduce the size of the images, and hence, alleviate data storage requirements. However, this step is not exempt of computationally complex matrix operations, such as the computation of the eigenvalues and the eigenvectors of large and dense matrices. Hence, for the aforementioned applications in which prompt replies are mandatory, this dimensionality reduction must be considerably accelerated, typically through the utilization of high-performance computing platforms. For this purpose, reconfigurable hardware solutions such as field-programmable gate arrays have been consolidated during the last years as one of the standard choices for the fast processing of hyperspectral remotely sensed images due to their smaller size, weight and power consumption when compared with other high-performance computing systems. In this paper, we propose the implementation in reconfigurable hardware of the principal component analysis (PCA) algorithm to carry out the dimensionality reduction in hyperspectral images. Experimental results demonstrate that our hardware version of the PCA algorithm significantly outperforms a commercial software version, which makes our reconfigurable system appealing for onboard hyperspectral data processing. Furthermore, our implementation exhibits real-time performance with regard to the time that the targeted hyperspectral instrument takes to collect the image data.
机译：遥感高光谱成像是一个非常活跃的研究领域，在最近的科学文献中做出了许多贡献。从计算的角度来看，对这些图像的分析代表了极其复杂的过程，这主要是由于数据的高维度和用于处理高光谱图像的最新算法的固有复杂性。在需要实时响应的应用中，例如火灾追踪，自然灾害的预防和监控，化学泄漏以及其他环境污染，这种计算成本代表了一个重大劣势。这些算法中的许多算法都将降维，以消除分析中的高光谱图像中的噪声和冗余信息，作为完全处理高光谱图像的基本步骤之一。因此，可以显着减小图像的尺寸，从而减轻数据存储要求。但是，此步骤不能免除计算复杂的矩阵运算，例如大而密集矩阵的特征值和特征向量的计算。因此，对于前面提到的其中必须迅速答复的应用，通常必须通过使用高性能计算平台来大大加快降维的速度。为此，近几年来，诸如现场可编程门阵列之类的可重构硬件解决方案已被合并为快速处理高光谱遥感图像的标准选择之一，因为与其他同类产品相比，它们具有更小的尺寸，重量和功耗。高性能计算系统。在本文中，我们提出了在主成分分析（PCA）算法的可重构硬件中的实现，以实现高光谱图像的降维。实验结果表明，我们的PCA算法的硬件版本明显优于商业软件版本，这使我们的可重新配置系统吸引了机载高光谱数据处理。此外，我们的实现相对于目标高光谱仪器收集图像数据所花费的时间表现出实时性能。

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