Loading…
A Method for Assessing Spectral Image Utility
The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then...
Saved in:
Published in: | IEEE transactions on geoscience and remote sensing 2009-06, Vol.47 (6), p.1698-1706 |
---|---|
Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | cdi_FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3 |
---|---|
cites | cdi_FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3 |
container_end_page | 1706 |
container_issue | 6 |
container_start_page | 1698 |
container_title | IEEE transactions on geoscience and remote sensing |
container_volume | 47 |
creator | Stefanou, M.S. Kerekes, J.P. |
description | The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then discussing differences for spectral imagery. We define spectral image utility for the subpixel target detection application as the area under the receiver operating curve summarized across a range of target detection scenario parameters. We propose a new approach to assessing the utility of any spectral image for any target type and size and detection algorithm. Using six airborne hyperspectral images, we demonstrate the sensitivity of the assessed image utility to various target detection scenario parameters and show the flexibility of this approach as a tool to answer specific user information requirements. The results of this investigation lead to a better understanding of spectral image information vis-a-vis target detection performance and provide a step toward quantifying the ability of a spectral image to satisfy information exploitation requirements. |
doi_str_mv | 10.1109/TGRS.2008.2006364 |
format | article |
fullrecord | <record><control><sourceid>proquest_ieee_</sourceid><recordid>TN_cdi_proquest_miscellaneous_36331834</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>4783039</ieee_id><sourcerecordid>869849638</sourcerecordid><originalsourceid>FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3</originalsourceid><addsrcrecordid>eNp9kE1LAzEURYMoWKs_QNwMgrqamu-PZSlaCxXB1nXIZJI6ZTpTk-mi_94MLV24cPPe4p174R0AbhEcIQTV83L6uRhhCGU_OOH0DAwQYzKHnNJzMIBI8RxLhS_BVYxrCBFlSAxAPs7eXffdlplvQzaO0cVYNatssXW2C6bOZhuzctlXV9VVt78GF97U0d0c9xAsXl-Wk7d8_jGdTcbz3BLJurz03BlGC8SkZERYSylFtDQlFpjggmFiOC8cEyX2xpZUIq8K45ViJRGGDMHToXUb2p-di53eVNG6ujaNa3dRS64kVZzIRD7-SxJOCJKEJvD-D7hud6FJP2jJBE2Y6tvQAbKhjTE4r7eh2piw1wjq3rLuLevesj5aTpmHY7GJ1tQ-mMZW8RTESFDCCEzc3YGrnHOnMxUy3RT5BUdpgzU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>857463398</pqid></control><display><type>article</type><title>A Method for Assessing Spectral Image Utility</title><source>IEEE Xplore (Online service)</source><creator>Stefanou, M.S. ; Kerekes, J.P.</creator><creatorcontrib>Stefanou, M.S. ; Kerekes, J.P.</creatorcontrib><description>The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then discussing differences for spectral imagery. We define spectral image utility for the subpixel target detection application as the area under the receiver operating curve summarized across a range of target detection scenario parameters. We propose a new approach to assessing the utility of any spectral image for any target type and size and detection algorithm. Using six airborne hyperspectral images, we demonstrate the sensitivity of the assessed image utility to various target detection scenario parameters and show the flexibility of this approach as a tool to answer specific user information requirements. The results of this investigation lead to a better understanding of spectral image information vis-a-vis target detection performance and provide a step toward quantifying the ability of a spectral image to satisfy information exploitation requirements.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2008.2006364</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Airborne sensing ; Applied geophysics ; Detection algorithms ; Earth sciences ; Earth, ocean, space ; Exact sciences and technology ; Flexibility ; Hyperspectral imagery ; Hyperspectral imaging ; Hyperspectral sensors ; Image color analysis ; Image sampling ; Image sensors ; Internal geophysics ; Object detection ; Particle measurements ; Receivers ; Spatial resolution ; Spectra ; spectral image analysis ; spectral image utility ; Spectroscopy ; Target detection ; Utilities</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2009-06, Vol.47 (6), p.1698-1706</ispartof><rights>2009 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2009</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3</citedby><cites>FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4783039$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,778,782,27911,27912,54783</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21743530$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Stefanou, M.S.</creatorcontrib><creatorcontrib>Kerekes, J.P.</creatorcontrib><title>A Method for Assessing Spectral Image Utility</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then discussing differences for spectral imagery. We define spectral image utility for the subpixel target detection application as the area under the receiver operating curve summarized across a range of target detection scenario parameters. We propose a new approach to assessing the utility of any spectral image for any target type and size and detection algorithm. Using six airborne hyperspectral images, we demonstrate the sensitivity of the assessed image utility to various target detection scenario parameters and show the flexibility of this approach as a tool to answer specific user information requirements. The results of this investigation lead to a better understanding of spectral image information vis-a-vis target detection performance and provide a step toward quantifying the ability of a spectral image to satisfy information exploitation requirements.</description><subject>Airborne sensing</subject><subject>Applied geophysics</subject><subject>Detection algorithms</subject><subject>Earth sciences</subject><subject>Earth, ocean, space</subject><subject>Exact sciences and technology</subject><subject>Flexibility</subject><subject>Hyperspectral imagery</subject><subject>Hyperspectral imaging</subject><subject>Hyperspectral sensors</subject><subject>Image color analysis</subject><subject>Image sampling</subject><subject>Image sensors</subject><subject>Internal geophysics</subject><subject>Object detection</subject><subject>Particle measurements</subject><subject>Receivers</subject><subject>Spatial resolution</subject><subject>Spectra</subject><subject>spectral image analysis</subject><subject>spectral image utility</subject><subject>Spectroscopy</subject><subject>Target detection</subject><subject>Utilities</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEURYMoWKs_QNwMgrqamu-PZSlaCxXB1nXIZJI6ZTpTk-mi_94MLV24cPPe4p174R0AbhEcIQTV83L6uRhhCGU_OOH0DAwQYzKHnNJzMIBI8RxLhS_BVYxrCBFlSAxAPs7eXffdlplvQzaO0cVYNatssXW2C6bOZhuzctlXV9VVt78GF97U0d0c9xAsXl-Wk7d8_jGdTcbz3BLJurz03BlGC8SkZERYSylFtDQlFpjggmFiOC8cEyX2xpZUIq8K45ViJRGGDMHToXUb2p-di53eVNG6ujaNa3dRS64kVZzIRD7-SxJOCJKEJvD-D7hud6FJP2jJBE2Y6tvQAbKhjTE4r7eh2piw1wjq3rLuLevesj5aTpmHY7GJ1tQ-mMZW8RTESFDCCEzc3YGrnHOnMxUy3RT5BUdpgzU</recordid><startdate>20090601</startdate><enddate>20090601</enddate><creator>Stefanou, M.S.</creator><creator>Kerekes, J.P.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>7SP</scope><scope>F28</scope></search><sort><creationdate>20090601</creationdate><title>A Method for Assessing Spectral Image Utility</title><author>Stefanou, M.S. ; Kerekes, J.P.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Airborne sensing</topic><topic>Applied geophysics</topic><topic>Detection algorithms</topic><topic>Earth sciences</topic><topic>Earth, ocean, space</topic><topic>Exact sciences and technology</topic><topic>Flexibility</topic><topic>Hyperspectral imagery</topic><topic>Hyperspectral imaging</topic><topic>Hyperspectral sensors</topic><topic>Image color analysis</topic><topic>Image sampling</topic><topic>Image sensors</topic><topic>Internal geophysics</topic><topic>Object detection</topic><topic>Particle measurements</topic><topic>Receivers</topic><topic>Spatial resolution</topic><topic>Spectra</topic><topic>spectral image analysis</topic><topic>spectral image utility</topic><topic>Spectroscopy</topic><topic>Target detection</topic><topic>Utilities</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stefanou, M.S.</creatorcontrib><creatorcontrib>Kerekes, J.P.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Electronics & Communications Abstracts</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><jtitle>IEEE transactions on geoscience and remote sensing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Stefanou, M.S.</au><au>Kerekes, J.P.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Method for Assessing Spectral Image Utility</atitle><jtitle>IEEE transactions on geoscience and remote sensing</jtitle><stitle>TGRS</stitle><date>2009-06-01</date><risdate>2009</risdate><volume>47</volume><issue>6</issue><spage>1698</spage><epage>1706</epage><pages>1698-1706</pages><issn>0196-2892</issn><eissn>1558-0644</eissn><coden>IGRSD2</coden><abstract>The utility of an image is an attribute that describes the ability of that image to satisfy performance requirements for a particular application. This paper establishes the context for spectral image utility by first reviewing traditional approaches to assessing panchromatic image utility and then discussing differences for spectral imagery. We define spectral image utility for the subpixel target detection application as the area under the receiver operating curve summarized across a range of target detection scenario parameters. We propose a new approach to assessing the utility of any spectral image for any target type and size and detection algorithm. Using six airborne hyperspectral images, we demonstrate the sensitivity of the assessed image utility to various target detection scenario parameters and show the flexibility of this approach as a tool to answer specific user information requirements. The results of this investigation lead to a better understanding of spectral image information vis-a-vis target detection performance and provide a step toward quantifying the ability of a spectral image to satisfy information exploitation requirements.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TGRS.2008.2006364</doi><tpages>9</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0196-2892 |
ispartof | IEEE transactions on geoscience and remote sensing, 2009-06, Vol.47 (6), p.1698-1706 |
issn | 0196-2892 1558-0644 |
language | eng |
recordid | cdi_proquest_miscellaneous_36331834 |
source | IEEE Xplore (Online service) |
subjects | Airborne sensing Applied geophysics Detection algorithms Earth sciences Earth, ocean, space Exact sciences and technology Flexibility Hyperspectral imagery Hyperspectral imaging Hyperspectral sensors Image color analysis Image sampling Image sensors Internal geophysics Object detection Particle measurements Receivers Spatial resolution Spectra spectral image analysis spectral image utility Spectroscopy Target detection Utilities |
title | A Method for Assessing Spectral Image Utility |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T01%3A26%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_ieee_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20Method%20for%20Assessing%20Spectral%20Image%20Utility&rft.jtitle=IEEE%20transactions%20on%20geoscience%20and%20remote%20sensing&rft.au=Stefanou,%20M.S.&rft.date=2009-06-01&rft.volume=47&rft.issue=6&rft.spage=1698&rft.epage=1706&rft.pages=1698-1706&rft.issn=0196-2892&rft.eissn=1558-0644&rft.coden=IGRSD2&rft_id=info:doi/10.1109/TGRS.2008.2006364&rft_dat=%3Cproquest_ieee_%3E869849638%3C/proquest_ieee_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c385t-df6ea54b1588537cc44414dad27232b523a66be57d2facd481f9baf995d37a3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=857463398&rft_id=info:pmid/&rft_ieee_id=4783039&rfr_iscdi=true |