Development of an efficient automated hyperspectral processing system using embedded computing

Matthew S. Brown; Eli Glaser; Scott Grassinger; Ambrose Slone; Mark Salvador

doi:10.1117/12.918667

24 May 2012 Development of an efficient automated hyperspectral processing system using embedded computing

Matthew S. Brown, Eli Glaser, Scott Grassinger, Ambrose Slone, Mark Salvador

Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII; 839018 (2012) https://doi.org/10.1117/12.918667
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Automated hyperspectral image processing enables rapid detection and identification of important military targets from hyperspectral surveillance and reconnaissance images. The majority of this processing is done using ground-based CPUs on hyperspectral data after it has been manually exfiltrated from the mobile sensor platform. However, by utilizing high-performance, on-board processing hardware, the data can be immediately processed, and the exploitation results can be distributed over a low-bandwidth downlink, allowing rapid responses to situations as they unfold. Additionally, transitioning to higher-performance and more-compact processing architectures such as GPUs, DSPs, and FPGAs will allow the size, weight, and power (SWaP) demands of the system to be reduced. This will allow the next generation of hyperspectral imaging and processing systems to be deployed on a much wider range of smaller manned and unmanned vehicles. In this paper, we present results on the development of an automated, near-real-time hyperspectral processing system using a commercially available NVIDIA® Telsa™ GPU. The processing chain utilizes GPU-optimized implementations of well-known atmospheric-correction, anomaly-detection, and target-detection algorithms in order to identify targetmaterial spectra from a hyperspectral image. We demonstrate that the system can return target-detection results for HYDICE data with 308×1280 pixels and 145 bands against 30 target spectra in less than four seconds.

Citation Download Citation

Matthew S. Brown, Eli Glaser, Scott Grassinger, Ambrose Slone, and Mark Salvador "Development of an efficient automated hyperspectral processing system using embedded computing", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839018 (24 May 2012); https://doi.org/10.1117/12.918667

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