Remote classification from an airborne camera using image super-resolution

Matthew Woods*, Aggelos Katsaggelos

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The image processing technique known as super-resolution (SR), which attempts to increase the effective pixel sampling density of a digital imager, has gained rapid popularity over the last decade. The majority of literature focuses on its ability to provide results that are visually pleasing to a human observer. In this paper, we instead examine the ability of SR to improve the resolution-critical capability of an imaging system to perform a classification task from a remote location, specifically from an airborne camera. In order to focus the scope of the study, we address and quantify results for the narrow case of text classification. However, we expect the results generalize to a large set of related, remote classification tasks. We generate theoretical results through simulation, which are corroborated by experiments with a camera mounted on a DJI Phantom 3 quadcopter.

Original languageEnglish (US)
Pages (from-to)203-215
Number of pages13
JournalJournal of the Optical Society of America A: Optics and Image Science, and Vision
Volume34
Issue number2
DOIs
StatePublished - Feb 1 2017

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Computer Vision and Pattern Recognition

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