An effective thin cloud removal procedure for visible remote sensing images

Shen, Huanfeng; Li, Huifang; Yan, Qiurong; Zhang, Liangpei; Yuan, Qiangqiang

doi:10.1016/j.isprsjprs.2014.06.011

Cited by 144 publications

(62 citation statements)

References 23 publications

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“…If the underlying surfaces of the thin cloud have a high reflectance, the cloudy pixels are bright. Otherwise, the brightness of the thin cloudy pixels may be close to that of the bright and cloudiness pixels [13]. To fix this problem, the HOT test developed by Zhang et al [48] was used here to detect haze or thin clouds.…”

Section: Initial Spectral Test For Cloud and Snowmentioning

confidence: 99%

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

Bian

Qiannan

et al. 2016

Remote Sensing

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It is highly desirable to accurately detect the clouds in satellite images before any kind of applications. However, clouds and snow discrimination in remote sensing images is a challenging task because of their similar spectral signature. The shortwave infrared (SWIR, e.g., Landsat TM 1.55-1.75 µm band) band is widely used for the separation of cloud and snow. However, for some sensors such as the CBERS-2 (China-Brazil Earth Resources Satellite), CBERS-4 and HJ-1A/B (HuanJing (HJ), which means environment in Chinese) that are designed without SWIR band, such methods are no longer practical. In this paper, a new practical method was proposed to discriminate clouds from snow through combining the spectral reflectance with the spatio-temporal contextual information. Taking the Mt. Gongga region, where there is frequent clouds and snow cover, in China as a case area, the detailed methodology was introduced on how to use the 181 scenes of HJ-1A/B CCD images in the year 2011 to discriminate clouds and snow in these images. Visual inspection revealed that clouds and snow pixels can be accurately separated by the proposed method. The pixel-level quantitative accuracy validation was conducted by comparing the detection results with the reference cloud masks generated by a random-tile validation scheme. The pixel-level validation results showed that the coefficient of determination (R 2 ) between the reference cloud masks and the detection results was 0.95, and the average overall accuracy, precision and recall for clouds were 91.32%, 85.33% and 81.82%, respectively. The experimental results confirmed that the proposed method was effective at providing reasonable cloud mask for the SWIR-lacking HJ-1A/B CCD images. Since HJ-1A/B have been in orbit for over seven years and these satellites still run well, the proposed method is helpful for the cloud mask generation of the historical archive HJ-1A/B images and even similar sensors.

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Section: Initial Spectral Test For Cloud and Snowmentioning

confidence: 99%

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

Bian

Qiannan

et al. 2016

Remote Sensing

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“…On the other hand, due to the cloud contamination, there are many gaps in satellite remote sensing data Shen et al, 2014). The satellite-based PM2.5 estimates are usually seamed in space.…”

Section: Introductionmentioning

confidence: 99%

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING

Zhang

Shen

et al. 2018

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Satellite remote sensing has been reported to be a promising approach for the monitoring of atmospheric PM2.5. However, the satellite-based monitoring of ground-level PM2.5 is still challenging. First, the previously used polar-orbiting satellite observations, which can be usually acquired only once per day, are hard to monitor PM2.5 in real time. Second, many data gaps exist in satellitederived PM2.5 due to the cloud contamination. In this paper, the hourly geostationary satellite (i.e., Harawari-8) observations were adopted for the real-time monitoring of PM2.5 in a deep learning architecture. On this basis, the satellite-derived PM2.5 in conjunction with ground PM2.5 measurements are incorporated into a spatio-temporal fusion model to fill the data gaps. Using Wuhan Urban Agglomeration as an example, we have successfully derived the real-time and seamless PM2.

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“…The filtering approach operates in the frequency domain. As clouds are usually made of low frequency components, a high-pass filter such as the homomorphism filter [5,6] is capable of removing the clouds after filtering the data. However, the cut-off frequency to separate the high and low frequency components is empirically determined.…”

Section: Introductionmentioning

confidence: 99%

Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

Shen

Wang

et al. 2015

Remote Sensing

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An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a cloud-free image, changes of histogram curves validated the algorithm. Scatterplots were generated and linear regression performed for the reflectance values of each band before and after the algorithm, and compared to those of the reference image. Band-by-band, results in cloud removal were acceptable. The algorithm had little effect on pixels in cloud-free areas after the analyses of histograms, scatterplots, and linear regression equations. Finally, the algorithm was applied to various land use and land cover types and cloud conditions, and to a full Landsat-8 scene yielding satisfactory results efficiently.

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An effective thin cloud removal procedure for visible remote sensing images

Cited by 144 publications

References 23 publications

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING

Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

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An effective thin cloud removal procedure for visible remote sensing images

Cited by 144 publications

References 23 publications

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

Cloud and Snow Discrimination for CCD Images of HJ-1A/B Constellation Based on Spectral Signature and Spatio-Temporal Context

REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM2.5 USING SATELLITE REMOTE SENSING

Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

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REAL-TIME AND SEAMLESS MONITORING OF GROUND-LEVEL PM_2.5 USING SATELLITE REMOTE SENSING