Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques

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Abstract

The use of aerial hyperspectral imagery for the purpose of remote sensing is a rapidly growing research area. Currently, targets are generally detected by looking for distinct spectral features of the objects under surveillance. For example, a camouflaged vehicle, deliberately designed to blend into background trees and grass in the visible spectrum, can be revealed using spectral features in the near-infrared spectrum. This work aims to develop improved target detection methods, using a two-stage approach, firstly by development of a physics-based atmospheric correction algorithm to convert radiance into reflectance hyperspectral image data and secondly by use of improved outlier detection techniques. In this paper the use of the Percentage Occupancy Hit or Miss Transform is explored to provide an automated method for target detection in aerial hyperspectral imagery.
Original languageEnglish
Title of host publicationProc. SPIE 9844, Automatic Target Recognition XXVI
Number of pages10
DOIs
Publication statusPublished - 12 May 2016
EventSPIE Defense + Security 2016 - Baltimore, United States
Duration: 17 Apr 201621 Apr 2016

Conference

ConferenceSPIE Defense + Security 2016
CountryUnited States
CityBaltimore
Period17/04/1621/04/16

Keywords

  • hyperspectral imaging (HSI)
  • remote sensing
  • Sequential Maximum Angle Convex Cone (SMACC)
  • hit or miss transform
  • Percentage Occupancy Hit or Miss Transform
  • outlier detection
  • target detection

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