Real-Time Independent Components Analysis for Dimensional Reduction of Hyperspectral Images Using Reconfigurable Hardware

Hyperspectral image analysis represents an extremely complex procedure from a computational point of view, mainly due to the high dimensionality of the data. This computational cost represents a significant disadvantage in applications that require a real-time response, such as fire monitoring, prevention and monitoring of natural disasters, chemical spills, and other environmental pollutants. Dimensional reduction allows us to decrease the size of the image while preserving the important discriminating features, by eliminating redundant data or noise. Due to their reduced size, weight, and power consumption when compared to other high-performance computing systems, reconfigurable hardware solutions, such as field-programmable gate arrays, have been consolidated in recent years as one of the standard options for the quick processing of hyperspectral remotely sensed images. In this paper, we have implemented an optimized hardware version of the Fast Independent Component Analysis (FastICA) version for dimensional reduction of hyperspectral images using an FPGA device. Our implementation achieves a 34 × speedup compared with its equivalent software version, achieving the objective of real-time processing considering the capture time of the AVIRIS spectral sensor.