{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:40:45Z","timestamp":1760222445368,"version":"build-2065373602"},"reference-count":13,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2009,4,27]],"date-time":"2009-04-27T00:00:00Z","timestamp":1240790400000},"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>Detecting and identifying weak gaseous plumes using thermal imaging data is complicated by many factors. There are several methods currently being used to detect plumes. They can be grouped into two categories: those that use a chemical spectral library and those that don\u2019t. The approaches that use chemical libraries include physics-based least squares methods (matched filter). They are \u201coptimal\u201d only if the plume chemical is actually in the search library but risk missing chemicals not in the library. The methods that don\u2019t use a chemical spectral library are based on a statistical or data analytical transformation applied to the data. These include principle components, independent components, entropy, Fourier transform, and others. These methods do not explicitly take advantage of the physics of the signal formulation process and therefore don\u2019t exploit all available information in the data. This paper describes generalized least squares detection using gas spectra, presents a new detection method using basis vectors, and compares detection images resulting from applying both methods to synthetic hyperspectral data.<\/jats:p>","DOI":"10.3390\/s90503205","type":"journal-article","created":{"date-parts":[[2009,4,27]],"date-time":"2009-04-27T10:24:13Z","timestamp":1240827853000},"page":"3205-3217","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Detection of Gaseous Plumes using Basis Vectors"],"prefix":"10.3390","volume":"9","author":[{"given":"Lawrence","family":"Chilton","sequence":"first","affiliation":[{"name":"PO Box 999, Pacific Northwest National Laboratory, Richland, WA 99352, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephen","family":"Walsh","sequence":"additional","affiliation":[{"name":"PO Box 999, Pacific Northwest National Laboratory, Richland, WA 99352, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2009,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1721","DOI":"10.3390\/s6121721","article-title":"Overview of physical models and statistical approaches for weak gaseous plume detection using passive infrared hyperspectral imagery","volume":"6","author":"Burr","year":"2006","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1117\/12.541641","article-title":"Gas plume species identification by regression analysis","volume":"5425","author":"Pogorzala","year":"2004","journal-title":"Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X. Proc. SPIE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1142\/S0129156407004990","article-title":"Detection of gaseous effluents from airborned lwir hyperspectral imagery using physics-based signatures","volume":"17","author":"Messinger","year":"2007","journal-title":"Int. J. High Speed Electron. Sys."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1117\/12.542442","article-title":"Scene analysis and detection in thermal infrared remote sensing using independent component analysis","volume":"5439","author":"Foy","year":"2004","journal-title":"Proc. SPIE"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"905","DOI":"10.3390\/s7060905","article-title":"Nonlinear bayesian algorithms for gas plume detection and estimation from hyper-spectral thermal image data","volume":"7","author":"Heasler","year":"2007","journal-title":"Sensors"},{"key":"ref_6","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_7","unstructured":"Schott, J. (1997). Remote Sensing., Oxford Press."},{"key":"ref_8","unstructured":"Liou, K.N. (2002). An Introduction to Atmospheric Radiation, Academic Press. 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SPIE."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/5\/3205\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T22:10:19Z","timestamp":1760220619000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/9\/5\/3205"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009,4,27]]},"references-count":13,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2009,5]]}},"alternative-id":["s90503205"],"URL":"https:\/\/doi.org\/10.3390\/s90503205","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2009,4,27]]}}}