太阳成8722(中国)有限公司-GREEN NO.1

标题: A Spatial-Spectral Adaptive Haze Removal Method for Visible Remote Sensing Images

作者: Shen, HF (Shen, Huanfeng); Zhang, C (Zhang, Chi); Li, HF (Li, Huifang); Yuan, Q (Yuan, Quan); Zhang, LP (Zhang, Liangpei)

来源出版物: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING  卷: 58  期: 9  页: 6168-6180  DOI: 10.1109/TGRS.2020.2974807  出版年: SEPT 2020  

摘要: Visible remotely sensed images usually suffer from the haze, which contaminates the surface radiation and degrades the data quality in both spatial and spectral dimensions. This study proposes a spatial-spectral adaptive haze removal method for visible remote sensing images to resolve spatial and spectral problems. Spatial adaptation is considered from global and local aspects. A globally nonuniform atmospheric light model is constructed to depict spatially varied atmospheric light. Moreover, a bright pixel index is built to extract local bright surfaces for transmission correction. Spectral adaptation is performed by exploring the relationships between image gradients and transmissions among bands to estimate spectrally varied transmission. Visible remote sensing images featuring different land covers and haze distributions were collected for synthetic and real experiments. Accordingly, four haze removal methods were selected for comparison. Visually, the results of the proposed method are completely free from haze and colored naturally in all experiments. These outcomes are nearly the same as the ground truth in the synthetic experiments. Quantitatively, the mean-absolute-error, root-mean-square-error, and spectral angle are the smallest, and the coefficient-of-determination (R2) is the largest among the five methods in the synthetic experiments. R2, structural similarity index measure, and the correlation coefficient between the result of the proposed method and the reference image are closest to 1 in the real data experiments. All experimental analyses demonstrate that the proposed method is effective in removing haze and recovering ground information faithfully under different scenes.

入藏号: WOS:000564455700013

语言: English

文献类型: Article

作者关键词: Atmospheric modeling; Remote sensing; Scattering; Distortion; Indexes; Image color analysis; Bright pixel index (BPI); dark channel prior (DCP); haze removal; spatial-spectral adaptive

KeyWords Plus: THIN CLOUD REMOVAL; LAND-SURFACE IMAGERY; ATMOSPHERIC CORRECTION; SATELLITE DATA; ALGORITHM; MODEL

地址: [Shen, Huanfeng; Zhang, Chi; Li, Huifang] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Peoples R China.

[Shen, Huanfeng] Wuhan Univ, Collaborat Innovat Ctr Geospatial Technol, Wuhan 430072, Peoples R China.

[Yuan, Quan] Guangdong OPPO Mobile Telecommun Corp Ltd, Dongguan 523000, Peoples R China.

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

通讯作者地址: Shen, HF (corresponding author)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Peoples R China.

电子邮件地址: shenhf@whu.edu.cn; zhangchi9502@outlook.com; huifangli@whu.edu.cn; 1127905893@qq.com; zlp62@whu.edu.cn

影响因子：5.855