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  1. 060 工学部
  2. 10 学術雑誌論文
  3. 10 査読済論文
  1. 0 資料タイプ別
  2. 01 学術雑誌論文

Capability Assessment of Fully Polarimetric ALOS-PALSAR data for Discriminating Wet Snow from Other Scattering Types in Mountainous Regions

http://hdl.handle.net/10191/25966
http://hdl.handle.net/10191/25966
a2dff37c-c914-445e-9859-f0e9c6849dfa
名前 / ファイル ライセンス アクション
IEEETGRS_52(2)_1177-1196.pdf IEEETGRS_52(2)_1177-1196.pdf (3.4 MB)
Item type 学術雑誌論文 / Journal Article(1)
公開日 2018-09-11
タイトル
タイトル Capability Assessment of Fully Polarimetric ALOS-PALSAR data for Discriminating Wet Snow from Other Scattering Types in Mountainous Regions
タイトル
言語 en
タイトル Capability Assessment of Fully Polarimetric ALOS-PALSAR data for Discriminating Wet Snow from Other Scattering Types in Mountainous Regions
言語
言語 eng
キーワード
主題Scheme Other
主題 Remote sensing
キーワード
主題Scheme Other
主題 ALOS-PALSAR
キーワード
主題Scheme Other
主題 snow
キーワード
主題Scheme Other
主題 Himalaya
キーワード
主題Scheme Other
主題 Glacier
キーワード
主題Scheme Other
主題 H/A/ α
キーワード
主題Scheme Other
主題 Wishart classifier
キーワード
主題Scheme Other
主題 Polarization Fraction
資源タイプ
資源 http://purl.org/coar/resource_type/c_6501
タイプ journal article
著者 Singh, Gulab

× Singh, Gulab

WEKO 5230

Singh, Gulab

Search repository
Venkataraman, Gopalan

× Venkataraman, Gopalan

WEKO 5231

Venkataraman, Gopalan

Search repository
山口, 芳雄

× 山口, 芳雄

WEKO 19

山口, 芳雄

Search repository
Park, Sang-Eun

× Park, Sang-Eun

WEKO 5233

Park, Sang-Eun

Search repository
抄録
内容記述タイプ Abstract
内容記述 This study examines the capability assessment of fully polarimetric L-band data for snow and non-snow area classification. The data sets used are the fully polarimetric Advanced Land Observation Satellite – Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) data, optical (ALOS-AVNIR) data close to the radar acquisition, and ENVISAT-ASAR data. Several parameters are used to discriminate snow from non-snow-covered areas in the Indian Himalaya region, including backscattering coefficients, the ratio of cross/co-polarized backscattering power and polarization fraction value. Supervised classification schemes are employed using polarimetric decomposition methods based on the complex Wishart classifier. The accuracy of the classification was found to be 97.95% for the Wishart supervised classification. Among various parameters and methods, it was found that the alternative newly proposed Polarization Fraction (PF) scheme, based on the implementation of fully polarimetric synthetic aperture radar (POL-SAR) data, yielded the best classification result in the absence of training samples. The PF value has been effective for discrimination of snow-covered from non-snow-covered areas, debris covered glacier, and vegetation. The results of this investigation show that L-band fully polarimetric SAR data provide considerable improvement but may not possess the optimal capability to discriminate snow from other inherent natural and man-made scatterers in heavy snow laden mountainous scenarios which may require fully polarimetric S-Band or C-Band POLSAR measurements.
書誌情報 IEEE transactions on geoscience and remote sensing
en : IEEE transactions on geoscience and remote sensing

巻 52, 号 2, p. 1177-1196, 発行日 2014-02
出版者
出版者 IEEE
ISSN
収録物識別子タイプ ISSN
収録物識別子 01962892
書誌レコードID
収録物識別子タイプ NCID
収録物識別子 AA00231483
DOI
識別子タイプ DOI
関連識別子 info:doi/10.1109/TGRS.2013.2248369
権利
権利情報 ©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
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