{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:38:56Z","timestamp":1760243936559,"version":"build-2065373602"},"reference-count":9,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2010,9,17]],"date-time":"2010-09-17T00:00:00Z","timestamp":1284681600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper describes a new method for predicting the detectability of thin gaseous plumes in hyperspectral images. The novelty of this method is the use of basis vectors for each of the spectral channels of a collection instrument to calculate noise-equivalent concentration-pathlengths instead of matching scene pixels to absorbance spectra of gases in a library. This method provides insight into regions of the spectrum where gas detection will be relatively easier or harder, as influenced by ground emissivity, temperature contrast, and the atmosphere. Our results show that data collection planning could be influenced by information about when potential plumes are likely to be over background segments that are most conducive to detection.<\/jats:p>","DOI":"10.3390\/s100908652","type":"journal-article","created":{"date-parts":[[2010,9,19]],"date-time":"2010-09-19T09:41:46Z","timestamp":1284889306000},"page":"8652-8662","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting the Detectability of Thin Gaseous Plumes in Hyperspectral Images Using Basis Vectors"],"prefix":"10.3390","volume":"10","author":[{"given":"Kevin K.","family":"Anderson","sequence":"first","affiliation":[{"name":"Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark F.","family":"Tardiff","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lawrence K.","family":"Chilton","sequence":"additional","affiliation":[{"name":"Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2010,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.3390\/s6111587","article-title":"Characterizing Clutter in the Context of Detecting Weak Gaseous Plumes in Hyperspectral Imagery","volume":"6","author":"Burr","year":"2006","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.3390\/s6121721","article-title":"Overview of Physical Models and Statistical Approaches forWeak Gaseous Plume Detection using Passive Infrared Hyperspectral Imagery","volume":"6","author":"Burr","year":"2006","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1117\/12.490164","article-title":"Estimation of trace vapor concentration-pathlength in plumes for remote sensing applications from hyperspectral images","volume":"5093","author":"Gallagher","year":"2003","journal-title":"Proc. SPIE"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1117\/12.604075","article-title":"Characterizing non-gaussian clutter and detecting weak gaseous plumes in hytperspectal imagery","volume":"5806","author":"Theiler","year":"2005","journal-title":"Proc. SPIE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3205","DOI":"10.3390\/s90503205","article-title":"Detection of gaseous plumes using basis vectors","volume":"9","author":"Chilton","year":"2009","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Boonmee, M, Schott, JR, and Messinger, DW (2006). Land surface temperature and emissivity retrieval from thermal infrared hyperspectral imagery. 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