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Dimensionality reduction of hyperspectral images using reconfigurable hardware

Remotely sensed hyperspectral imaging is a very active research area, with numerous contributions in the recent scientific literature. To carry out these investigations, it is necessary to collect large amounts of information that will be processed on satellite or airborne platforms using parallel processing techniques on multi core systems or Graphics Processing Units, trying to avoid excessive energy consumption. Due to the high dimensionality of the data, algorithms analyzing hyperspectral images have a high computational cost. This cost is 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, etc. To solve these problems, one of the solutions most used is the dimensional reduction, which removes noise and redundant information of images. Therefore, it is possible to reduce significantly the size of the images, and improve the complexity of the algorithms and data storage. Moreover, Field-Programmable Gate Arrays are specially recommended in remotely sensed applications that require real-time response due to their features such as reconfiguration, low consumption, compact size and high computing power on board. In this work, we propose the implementation in reconfigurable hardware of the principal component analysis (PCA) algorithm to carry out the dimensional reduction of hyperspectral images. Experimental results demonstrate that our hardware version of the PCA algorithm exhibits real-time performance.