Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images

Liang, Ye; Cao, Zhiguo; Xiao, Yang; Yang, Zhibiao

doi:10.1109/tgrs.2019.2917612

Cited by 36 publications

(29 citation statements)

References 37 publications

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“…The information provided by the sensors may be combined to increase the diversity of features extracted from the clouds [9] . Image processing is an important factor in the performance of a solar forecasting algorithm [10] . In the context of machine learning and image processing, the feature extraction algorithm may be adapted to different applications in solar problems [11] , [12] .…”

Section: Value Of the Datamentioning

confidence: 99%

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Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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The energy available in a microgrid that is powered by solar energy is tightly related to the weather conditions at the moment of generation. A very short-term forecast of solar irradiance provides the microgrid with the capability of automatically controlling the dispatch of energy. We propose a dataset to forecast Global Solar Irradiance (GSI) using a data acquisition system (DAQ) that simultaneously records sky imaging and GSI measurements, with the objective of extracting features from clouds and use them to forecast the power produced by a Photovoltaic (PV) system. The DAQ system is nicknamed the Girasol Machine (Girasol means Sunflower in Spanish). The sky imaging system consists of a longwave infrared (IR) camera and a visible (VI) light camera with a fisheye lens attached to it. The cameras are installed inside a weatherproof enclosure that it is mounted on a solar tracker. The tracker updates its pan and tilt every second using a solar position algorithm to maintain the Sun in the center of the IR and VI images. A pyranometer is situated on a horizontal mount next to the DAQ system to measure GSI. The dataset, composed of IR images, VI images, GSI measurements, and the Sun’s positions, has been tagged with timestamps.

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Section: Value Of the Datamentioning

confidence: 99%

Section: Value Of the Datamentioning

confidence: 99%

Girasol, a sky imaging and global solar irradiance dataset

et al. 2021

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“…Neural network and deep learning are also used in the research of ground-based cloud images, but in most cases, they are used for cloud recognition and cloud classification [12][13][14][15][16]. In fact, the prediction of cloud motion in a ground-based cloud image can also be directly obtained from the images generated by the neural network.…”

Section: Introductionmentioning

confidence: 99%

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Wang

et al. 2021

Remote Sensing

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Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

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“…To identify the most relevant color components, clustering and dimensionality reduction techniques were applied (Dev et al, 2014(Dev et al, , 2016. There are also studies on supervised learning techniques for classifying pixels in ASIs such as neural networks, support vector machines (SVMs), random forests and bayesian classifiers (Taravat et al, 2014;Cheng and Lin, 2017;Ye et al, 2019). Lately, also deep learning approaches using convolutional neural networks (CNNs) were presented (Dev et al, 2019;Xie et al, 2020;Song et al, 2020).…”

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confidence: 99%

“…In two studies, clouds were distinguished in thin and thick clouds (Dev et al, 2015(Dev et al, , 2019, however only considering a small dataset of 32 images of sky patches. To our knowledge, there is only one work of an extensive segmentation approach which is based on 9 cloud genera using 600 labeled ASIs (Ye et al, 2019). The authors propose to extract and transform a set of features for generated super-pixels and classify each of them using a SVM.…”

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confidence: 99%

Applying self-supervised learning for semantic cloud segmentation of all-sky images

Fabel¹,

Nouri²,

Wilbert³

et al. 2021

Preprint

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Abstract. Semantic segmentation of ground-based all-sky images (ASIs) can provide high-resolution cloud coverage information of distinct cloud types, applicable for meteorology, climatology and solar energy-related applications. Since the shape and appearance of clouds is variable and there is high similarity between cloud types, a clear classification is difficult. Therefore, most state-of-the-art methods focus on the distinction between cloudy- and cloudfree-pixels, without taking into account the cloud type. On the other hand, cloud classification is typically determined separately on image-level, neglecting the cloud's position and only considering the prevailing cloud type. Deep neural networks have proven to be very effective and robust for segmentation tasks, however they require large training datasets to learn complex visual features. In this work, we present a self-supervised learning approach to exploit much more data than in purely supervised training and thus increase the model's performance. In the first step, we use about 300,000 ASIs in two different pretext tasks for pretraining. One of them pursues an image reconstruction approach. The other one is based on the DeepCluster model, an iterative procedure of clustering and classifying the neural network output. In the second step, our model is fine-tuned on a small labeled dataset of 770 ASIs, of which 616 are used for training and 154 for validation. For each of them, a ground truth mask was created that classifies each pixel into clear sky, low-layer, mid-layer or high-layer cloud. To analyze the effectiveness of self-supervised pretraining, we compare our approach to randomly initialized and pretrained ImageNet weights, using the same training and validation sets. Achieving 85.8 % pixel-accuracy on average, our best self-supervised model outperforms the conventional approaches of random (78.3 %) and pretrained ImageNet initialization (82.1 %). The benefits become even more evident when regarding precision, recall and intersection over union (IoU) on the respective cloud classes, where the improvement is between 5 and 20 % points. Furthermore, we compare the performance of our best model on binary segmentation with a clear-sky library (CSL) from the literature. Our model outperforms the CSL by over 7 % points, reaching a pixel-accuracy of 95 %.

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Supervised Fine-Grained Cloud Detection and Recognition in Whole-Sky Images

Cited by 36 publications

References 37 publications

Girasol, a sky imaging and global solar irradiance dataset

Girasol, a sky imaging and global solar irradiance dataset

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Applying self-supervised learning for semantic cloud segmentation of all-sky images

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