科研成果
李慧芳的论文在IEEE GEOSCIENCE AND REMOTE SENSING LETTERS刊出
发布时间:2014-01-17 10:44:12     发布者:yz     浏览次数:

标题:A Principal Component Based Haze Masking Method for Visible Images作者:Li, Huifang; Zhang, Liangpei; Shen, Huanfeng

来源出版物:IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 卷:11 期:5 页:975-979 DOI:10.1109/LGRS.2013.2283792出版年:MAY 2014

摘要:Land surfaces are commonly obstructed by haze in remote sensing images, which reduces the available land cover information. Haze detection is therefore important for locating, avoiding, or restoring hazy regions. In this letter, a principal component (PC)-based haze masking (PCHM) method is developed for the masking of haze in visible remote sensing images covering land surfaces at middle latitudes. Owing to the evidence of haze in the second PC, the PCHM method results in accurate haze masks. The complete procedure comprises two steps: haze construction and spatial optimization. The validity of the PCHM method is demonstrated through its application to several hazy visible images clipped from Landsat Enhanced Thematic Mapper Plus scenes. The quantitative assessments verify the superiority of the proposed method over the haze optimized transformation method for the production of binary haze masks. In addition, the resulting haze masks are compared with a MODIS cloud product, which further proves the necessity and validity of the proposed method.

入藏号:WOS:000328708000018

文献类型:Article

语种:English

作者关键词:Haze masking, principal component (PC), spatial optimization, visible remote sensing images

扩展关键词:CLOUD-COVER ASSESSMENT; LANDSAT IMAGERY; REMOVAL

通讯作者地址:Li, Huifang; Wuhan Univ, Fac Sci, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址:l.huifang10@gmail.com; zlp62@whu.edu.cn; shenhf@whu.edu.cn

地址:

[Li, Huifang; Shen, Huanfeng]Wuhan Univ, Fac Sci, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

[Zhang, Liangpei] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China.

研究方向:Geochemistry & Geophysics; Engineering; Remote Sensing

ISSN:1545-598X

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