FPGA-Based Hyperspectral Lossy Compressor With Adaptive Distortion Feature for Unexpected Scenarios

Abstract

Lossy compression solutions have grown up during the past decades because of the increment of the data rate in the new-generation hyperspectral sensors; however, linear compression techniques include useless information on regions of little interest for the final application and, at the same time, scarce information on areas of interest. In this article, a transform-based lossy compressor, HyperLCA, has been extended to include a runtime adaptive distortion feature that brings multiple compression ratios in the same scenario. The solution has been designed to keep the same hardware-friendly feature, just as its previous version, specifically conceived to ease the deployment of the solution on reconfigurable hardware devices (FPGAs). The experiments demonstrate that the new version of the compressor is able to process 1024 × 1024 hyperspectral images and 180 spectral bands (377.5 MB) in 0.935 s with a power consumption of 1.145 W. In addition, experimental results also reveal that our architecture features high throughput (MSamples/s) and remarkable energy-efficiency (MB/s/W) tradeoffs, $10\times$ and $6\times$ greater than the best state-of-the-art solution, respectively.

Keywords

• Adaptive computing
• field-programmable gate array (FPGA)
• hyperspectral imaging
• lossy compression
• on-board processing