Abstract

Abstract. Cloud detection and cloud properties have substantial applications in weather forecast, signal attenuation analysis, and other cloud-related fields. Cloud image segmentation is the fundamental and important step in deriving cloud cover. However, traditional segmentation methods rely on low-level visual features of clouds and often fail to achieve satisfactory performance. Deep convolutional neural networks (CNNs) can extract high-level feature information of objects and have achieved remarkable success in many image segmentation fields. On this basis, a novel deep CNN model named SegCloud is proposed and applied for accurate cloud segmentation based on ground-based observation. Architecturally, SegCloud possesses a symmetric encoder–decoder structure. The encoder network combines low-level cloud features to form high-level, low-resolution cloud feature maps, whereas the decoder network restores the obtained high-level cloud feature maps to the same resolution of input images. The Softmax classifier finally achieves pixel-wise classification and outputs segmentation results. SegCloud has powerful cloud discrimination capability and can automatically segment whole-sky images obtained by a ground-based all-sky-view camera. The performance of SegCloud is validated by extensive experiments, which show that SegCloud is effective and accurate for ground-based cloud segmentation and achieves better results than traditional methods do. The accuracy and practicability of SegCloud are further proven by applying it to cloud cover estimation.

Highlights

  • Clouds are among the most common and important meteorological phenomena, covering over 66 % of the global surface (Rossow and Schiffer, 1991; Carslaw, 2009; Stephens, 2005; Zhao et al, 2019; Wang and Zhao, 2017)

  • Accurate cloud segmentation is a primary precondition for the cloud analysis of ground-based all-sky-view imag

  • Accurate cloud segmentation has become a topic of interest, and many algorithms have been recently proposed for the cloud analysis of ground-based all-sky-view imaging instruments (Long et al, 2006; Kreuter et al, 2009; Heinle et al, 2010; Liu et al, 2014, 2015)

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Summary

Introduction

Clouds are among the most common and important meteorological phenomena, covering over 66 % of the global surface (Rossow and Schiffer, 1991; Carslaw, 2009; Stephens, 2005; Zhao et al, 2019; Wang and Zhao, 2017). Accurate cloud segmentation has become a topic of interest, and many algorithms have been recently proposed for the cloud analysis of ground-based all-sky-view imaging instruments (Long et al, 2006; Kreuter et al, 2009; Heinle et al, 2010; Liu et al, 2014, 2015). Long et al (2006) and Kreuter et al (2009) proposed a fixed-threshold algorithm using the ratio of red and blue channel values to identify clouds from whole-sky images. SegCloud is characterized by powerful cloud discrimination and can automatically segment the obtained whole-sky images It improves the accuracy of cloud segmentation and avoids misrecognition caused by traditionally color-based threshold methods.

Data description
Cloud image segmentation approach
SegCloud architecture
Encoder network
Decoder network
Softmax classifier
Training details of SegCloud model
Experiment
Effectiveness of SegCloud model
Comparison with other methods
Application of SegCloud in cloud cover estimation
Findings
Conclusions
Full Text
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