An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands

Lv, Haitao; Wang, Yong; Shen, Yang

doi:10.1016/j.rse.2016.03.034

Cited by 72 publications

(15 citation statements)

References 19 publications

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“…In such cases, surface information is partly present and can be restored, e.g. using mathematical ( Xu et al, 2019 , Hu et al, 2015 ) or physical models ( Xu et al, 2016 , Lv et al, 2016 ). Multispectral methods have the advantage of exploiting information from the original scene without requiring additional data, but are limited to filmy, semi-transparent clouds.…”

Section: Introductionmentioning

confidence: 99%

Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

Meraner

Ebel

Zhu

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

273

138

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Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods. In this paper, a deep residual neural network architecture is designed to remove clouds from multispectral Sentinel-2 imagery. SAR-optical data fusion is used to exploit the synergistic properties of the two imaging systems to guide the image reconstruction. Additionally, a novel cloud-adaptive loss is proposed to maximize the retainment of original information. The network is trained and tested on a globally sampled dataset comprising real cloudy and cloud-free images. The proposed setup allows to remove even optically thick clouds by reconstructing an optical representation of the underlying land surface structure.

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Section: Introductionmentioning

confidence: 99%

Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

Meraner

Ebel

Zhu

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

273

138

View full text Add to dashboard Cite

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“…Compared with conventional images, remote sensing images are more vulnerable to cloud and fog occlusion and light changes. Especially considering that 66% of the earth's surface is often covered by cloud and fog [23], accurate detection of small target in remote sensing images under the interference of clouds and fog has become a problem that must be faced and solved [24,25]. Therefore, based on the VEDAI dataset, the VEDAI-Cloud dataset is constructed by artificially adding cloud interference, such as Figure 4.…”

Section: Dataset and Experimental Setupmentioning

confidence: 99%

A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection

Cao

Yang

Chen

2021

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Convolutional neural networks (CNNs) have shown strong learning capabilities in computer vision tasks such as classification and detection. Especially with the introduction of excellent detection models such as YOLO (V1, V2 and V3) and Faster R-CNN, CNNs have greatly improved detection efficiency and accuracy. However, due to the special angle of view, small size, few features, and complicated background, CNNs that performs well in the ground perspective dataset, fails to reach a good detection accuracy in the remote sensing image dataset. To this end, based on the YOLO V3 model, we used feature maps of different depths as detection outputs to explore the reasons for the poor detection rate of small targets in remote sensing images by deep neural networks. We also analyzed the effect of neural network depth on small target detection, and found that the excessive deep semantic information of neural network has little effect on small target detection. Finally, the verification on the VEDAI dataset shows, that the fusion of shallow feature maps with precise location information and deep feature maps with rich semantics in the CNNs can effectively improve the accuracy of small target detection in remote sensing images.

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“…Typical methods include filtering in the frequency domain [17,20], dark channel prior (DCP)-based methods [21][22][23][24], and spectral transformation-based methods [25][26][27][28]. These methods can remove clouds without any prior knowledge and can be combined with an RTM method to achieve an accurate correction [29]. However, statistical characteristics are not always suitable for the complex scenes acquired from different sensors.…”

Section: Related Workmentioning

confidence: 99%

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

Feng

et al. 2019

Remote Sensing

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Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.

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An empirical and radiative transfer model based algorithm to remove thin clouds in visible bands

Cited by 72 publications

References 19 publications

Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

A New Method of Different Neural Network Depth and Feature Map Size on Remote Sensing Small Target Detection

Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions

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