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Proceedings Paper

Software defined multi-spectral imaging for Arctic sensor networks
Author(s): Sam Siewert; Vivek Angoth; Ramnarayan Krishnamurthy; Karthikeyan Mani; Kenrick Mock; Surjith B. Singh; Saurav Srivistava; Chris Wagner; Ryan Claus; Matthew Demi Vis
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Paper Abstract

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.

Paper Details

Date Published: 17 May 2016
PDF: 15 pages
Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 98401V (17 May 2016); doi: 10.1117/12.2222966
Show Author Affiliations
Sam Siewert, Embry-Riddle Aeronautical Univ. (United States)
Univ. of Colorado Boulder (United States)
Vivek Angoth, Univ. of Colorado Boulder (United States)
Ramnarayan Krishnamurthy, Univ. of Colorado Boulder (United States)
Karthikeyan Mani, Univ. of Colorado Boulder (United States)
Kenrick Mock, Arctic Domain Awareness Ctr. (United States)
Univ. of Alaska Anchorage (United States)
Surjith B. Singh, Univ. of Colorado Boulder (United States)
Saurav Srivistava, Univ. of Colorado Boulder (United States)
Chris Wagner, Univ. of Colorado Boulder (United States)
Ryan Claus, Embry-Riddle Aeronautical Univ. (United States)
Matthew Demi Vis, Embry-Riddle Aeronautical Univ. (United States)


Published in SPIE Proceedings Vol. 9840:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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