Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

Parmes, Eija; Rauste, Yrjö; Molinier, Matthieu; Andersson, Kristin; Seitsonen, Lauri

doi:10.3390/rs9080806

Cited by 20 publications

(12 citation statements)

References 15 publications

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“…They faced difficulty for clouds and cloud shadows masking [70]. Hence, RGB and NIR (and SWIR for Sentinel 2) bands were used to detect cloud; the ratio of blue and green reflectance was employed to identify shadow [71]. Although the method did not use thermal bands, its performance reached similar accuracies to the VIIRS Cloud Mask [72,73] and VIIRS I-Band Cloud Mask [74] methods which used thermal bands [71].…”

Section: Data and Image Preprocessingmentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

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Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies.

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Section: Data and Image Preprocessingmentioning

confidence: 99%

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

et al. 2018

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“…Generally, the traditional methods can be divided into two types: threshold-based methods and multitemporalbased methods. Threshold-based methods are widely used to generate basic masks, and can distinguish cloudy from clear-sky pixels employing the spectral differences between dark land and clouds (Sun et al, 2018;Wang et al, 2016;Zhu et al, 2015;Parmes et al, 2017) but often fail to separate clouds from highlights. Multitemporal-based methods use clean images from different time periods within a certain area to produce clean synthetic images, then calculate the difference between cloudy images and clean images.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cloud Detection Method Based on Generative Adversarial Networks in Remote Sensing Images

et al. 2020

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

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Abstract. Clouds in optical remote sensing images seriously affect the visibility of background pixels and greatly reduce the availability of images. It is necessary to detect clouds before processing images. In this paper, a novel cloud detection method based on attentive generative adversarial network (Auto-GAN) is proposed for cloud detection. Our main idea is to inject visual attention into the domain transformation to detect clouds automatically. First, we use a discriminator (D) to distinguish between cloudy and cloud free images. Then, a segmentation network is used to detect the difference between cloudy and cloud-free images (i.e. clouds). Last, a generator (G) is used to fill in the different regions in cloud image in order to confuse the discriminator. Auto-GAN only requires images and their labels (1 for a cloud-free image, 0 for a cloudy image) in the training phase which is more time-saving to acquire than existing methods based on CNNs that require pixel-level labels. Auto-GAN is applied to cloud detection in Sentinel-2A Level 1C imagery. The results indicate that Auto-GAN method performs well in cloud detection over different land surfaces.

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“…Hence, the simple spectral threshold method offers an effective way in cloud detection and this type of method is regarded as the most popular and reliable approach. Thus, most typical cloud masking algorithms are based on spectral differences, such as the advanced very high resolution radiometer processing scheme over land and ocean (APOLLO; Derrien et al, 1993;Saunders & Kriebel, 1988), the cloud detection used in the International Satellite Cloud Climatology project (Rossow & Garder, 1993), the Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask product (MOD35; Ackerman et al, 1998), Visible Infrared Imager Radiometer Suite cloud detection (Hutchison et al, 2005;Parmes et al, 2017), Advanced Spaceborne Thermal Emission and Reflection Radiometer (Hulley & Hook, 2008), and Landsat cloud covers (Irish et al, 2006;Zi et al, 2018). In addition to the above-mentioned spectral-based method, the multitemporal approach (Champion, 2012;Hagolle et al, 2010;Zhu & Woodcock, 2014) is another widely used type of method for cloud detection.…”

Section: Introductionmentioning

confidence: 99%

Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields

et al. 2019

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Clouds and shadows pose a significant barrier for land surface optical and infrared remote sensing image processing and their various applications. The detection and removal of clouds and shadows from satellite images have always been critical preprocessing steps. To date, a variety of methods have been designed to solve this problem. Some require particular channels, while others are heavily dependent on the availability of temporally adjacent images (reference images). Moreover, many methods are too complex to use by common users. For those reasons, in this paper an alternative scheme for detecting clouds and shadows is proposed based on simulated top‐of‐atmosphere radiance fields. At the same time, a simple approach to remove clouds and shadows is also provided. The results indicate that the new method can properly identify both clouds and shadows in satellite images. Especially, it shows obvious advantage over the Moderate Resolution Imaging Spectroradiometer cloud product (MOD35) for shadow detection. Although the proposed cloud removal method is simple, the radiances of a contaminated image can be reasonably reconstructed with root‐mean‐square error < 3.0 W/m2·sr·μm and mean bias < 1.0 W/m2·sr·μm for all seven Moderate Resolution Imaging Spectroradiometer reflective bands for our case studies. These results prove the effectiveness of the proposed scheme in identifying and removing clouds and shadows from remotely sensed images. Meanwhile, these findings provide some new ideas for the remote sensing community, especially in the fields of cloud detection and image processing.

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Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands—Application to Suomi NPP VIIRS Images over Fennoscandia

Cited by 20 publications

References 15 publications

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017

Automatic Cloud Detection Method Based on Generative Adversarial Networks in Remote Sensing Images

Detection and Removal of Clouds and Associated Shadows in Satellite Imagery Based on Simulated Radiance Fields

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