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李慧芳、博士生罗爽的论文在SIGNAL PROCESSING刊出
发布时间:2019-10-08 11:39:48     发布者:易真     浏览次数:

标题: Shadow removal based on separated illumination correction for urban aerial remote sensing images

作者: Luo, S (Luo, Shuang); Shen, HF (Shen, Huanfeng); Li, HF (Li, Huifang); Chen, YM (Chen, Yumin)

来源出版物: SIGNAL PROCESSING  : 165  : 197-208  DOI: 10.1016/j.sigpro.2019.06.039  出版年: DEC 2019  

摘要: The presence of shadows in urban aerial images degrades the image quality and reduces the application accuracy. Removing shadows and recovering the ground information is therefore a crucial issue. The existing shadow removal methods can correct the shadow information, but the inconsistency between the corrected shadow and non-shadow areas is still obvious. A novel shadow removal method based on separated illumination correction is proposed in this paper, in which the shadow removal is only performed on the shadow-related illumination. A spatially adaptive weighted total variation model is constructed to obtain the shadow-related illumination and the shadow-free reflectance. The objects in the shadows are detected based on the reflectance, and object-oriented illumination correction is then implemented to compensate the shadow regions. The shadow removal results can be obtained by combining the corrected illumination and the reflectance. Three aerial remote sensing images were selected for the experiments, and two quantitative evaluation methods are introduced: the shadow standard deviation index and classification analysis. The results are shown and compared with four existing methods by visual and quantitative assessments, which indicate that the proposed method can yield more visually natural shadow-free images and show a better performance in the quantitative indices. (C) 2019 Elsevier B.V. All rights reserved.

入藏号: WOS:000485855000019

语言: English

文献类型: Article

作者关键词: Shadow removal; Spatially adaptive; Illumination correction; Aerial images

KeyWords Plus: RESOLUTION; RECONSTRUCTION; INFORMATION; MODEL

地址: [Luo, Shuang; Shen, Huanfeng; Li, Huifang; Chen, Yumin] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China.

[Shen, Huanfeng] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Hubei, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Key Lab Geog Informat Syst, Minist Educ, Wuhan, Hubei, Peoples R China.

通讯作者地址: Li, HF (通讯作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Hubei, Peoples R China.

电子邮件地址: huifangli@whu.edu.cn

影响因子:4.086


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