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

Andrew Young; Stephen Marshall; Alison Gray

doi:10.1117/12.2213530

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

Andrew Young, Stephen Marshall, Alison Gray

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

1 Citation (Scopus)

100 Downloads (Pure)

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 language	English
Title of host publication	Proc. SPIE 9844, Automatic Target Recognition XXVI
Number of pages	10
DOIs	https://doi.org/10.1117/12.2213530
Publication status	Published - 12 May 2016
Event	SPIE Defense + Security 2016 - Baltimore, United States Duration: 17 Apr 2016 → 21 Apr 2016

Conference

Conference	SPIE Defense + Security 2016
Country/Territory	United States
City	Baltimore
Period	17/04/16 → 21/04/16

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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10.1117/12.2213530

Young-etal-ATR2016-outlier-target-detection-aerial-hyperspectral-imagery-comparison-traditionalAccepted author manuscript, 2.15 MB

http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=2523235

Cite this

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

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. ",

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",

author = "Andrew Young and Stephen Marshall and Alison Gray",

note = "Andrew Young ; Stephen Marshall ; Alison Gray; Outlier and target detection in aerial hyperspectral imagery: a comparison of traditional and percentage occupancy hit or miss transform techniques. Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440S (May 12, 2016); doi:10.1117/12.2213530. 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.; SPIE Defense + Security 2016 ; Conference date: 17-04-2016 Through 21-04-2016",

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doi = "10.1117/12.2213530",

language = "English",

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N2 - 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.

AB - 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.

KW - hyperspectral imaging (HSI)

KW - remote sensing

KW - Sequential Maximum Angle Convex Cone (SMACC)

KW - hit or miss transform

KW - Percentage Occupancy Hit or Miss Transform

KW - outlier detection

KW - target detection

UR - http://spie.org/conferences-and-exhibitions/defense--commercial-sensing

U2 - 10.1117/12.2213530

DO - 10.1117/12.2213530

M3 - Conference contribution book

BT - Proc. SPIE 9844, Automatic Target Recognition XXVI

Y2 - 17 April 2016 through 21 April 2016

ER -

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

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