Computer, Electrical & Software Engineering
Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop-in-place installations in the Arctic. The prototype selected will be field tested in Alaska in the summer of 2016.
SPIE - Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Required Publisher’s Statement
Copyright 2016 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Scholarly Commons Citation
Sam B Siewert; Vivek Angoth; Ramnarayan Krishnamurthy; Karthikeyan Manim Kenrick Mock, Sirjith B. Singh; Saurav Srivistava; Chris Wagner; Ryan Claus; Matthew Demi Vis, "Software Defined Multi-Spectral Imaging for Arctic Sensor Networks," Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, Editors Miguel Velez-Reyes; David W. Messinger, 98401V (May 17, 2016). Author(s), “Paper Title,” Publication Title, Editors, Volume (Issue) Number, Article (or Page) Number, (Year).
Computer and Systems Architecture Commons, Multi-Vehicle Systems and Air Traffic Control Commons
Publisher required DOI format: https://doi.org/10.1117/12.2222966