Parametric Pipelined <italic>k</italic>-Means Implementation for Hyperspectral Processing on Spacecraft Embedded FPGA

Abstract

k-means stands out as one of the most common clustering algorithms, widely employed for classification in hyperspectral imaging. In this context, large amounts of data are gathered by sensors that are embedded into satellites with strict constraints in terms of power consumption, weight, physical space or radiation tolerance. Since communication bandwidth is also limited, data processing must be performed on board. However, meeting all those constraints also entails a significant tradeoff with computing performance. The aim of this work is clustering hyperspectral images in real time. Custom hardware has been designed with the objective of reducing overhead and maximizing performance, by exploiting several acceleration techniques. The implementation targets a space-grade Xilinx Kintex FPGA, which features low power consumption and is shielded against radiation. The design has a deep pipelined architecture, able to process all bands of each hyperspectral pixel in parallel. In consequence, it attains a throughput of 100 M hyperspectral pixels per second, even with a discrete use of FPGA resources. In addition, it is also fully parametric, with on-the-fly adaptation to different kinds of images and clustering configurations. Compared to previous implementations, ours takes advantage of a fully RTL design that avoids CPU bottlenecks and HLS design overheads. It also has a fixed throughput regardless of image or clustering properties, while having lower FPGA resource usage than performancewise equivalent implementations.

Keywords

• Clustering methods
• multidimensional signal processing
• reconfigurable architectures
• satellite applications