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- Publisher Website: 10.1109/LGRS.2004.824747
- Scopus: eid_2-s2.0-2442575186
- WOS: WOS:000208325200018
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Article: An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery
Title | An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery |
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Authors | |
Keywords | Aerosol Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) Atmospheric correction Hyperion Hyperspectral Imaging spectroscopy Remote sensing Water vapor |
Issue Date | 2004 |
Citation | IEEE Geoscience and Remote Sensing Letters, 2004, v. 1, n. 2, p. 112-117 How to Cite? |
Abstract | There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra. |
Persistent Identifier | http://hdl.handle.net/10722/321292 |
ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 1.248 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Liang, Shunlin | - |
dc.contributor.author | Fang, Hongliang | - |
dc.date.accessioned | 2022-11-03T02:17:56Z | - |
dc.date.available | 2022-11-03T02:17:56Z | - |
dc.date.issued | 2004 | - |
dc.identifier.citation | IEEE Geoscience and Remote Sensing Letters, 2004, v. 1, n. 2, p. 112-117 | - |
dc.identifier.issn | 1545-598X | - |
dc.identifier.uri | http://hdl.handle.net/10722/321292 | - |
dc.description.abstract | There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Geoscience and Remote Sensing Letters | - |
dc.subject | Aerosol | - |
dc.subject | Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) | - |
dc.subject | Atmospheric correction | - |
dc.subject | Hyperion | - |
dc.subject | Hyperspectral | - |
dc.subject | Imaging spectroscopy | - |
dc.subject | Remote sensing | - |
dc.subject | Water vapor | - |
dc.title | An improved atmospheric correction algorithm for hyperspectral remotely sensed imagery | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/LGRS.2004.824747 | - |
dc.identifier.scopus | eid_2-s2.0-2442575186 | - |
dc.identifier.volume | 1 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 112 | - |
dc.identifier.epage | 117 | - |
dc.identifier.isi | WOS:000208325200018 | - |