Feature extraction techniques for ground-based cloud type classification

Kliangsuwan, Thitinan; Heednacram, Apichat

doi:10.1016/j.eswa.2015.05.016

Cited by 18 publications

(7 citation statements)

References 22 publications

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“…This study presents the new cloud classification method based on a bag of micro-structures, whereas most state-ofthe-art methods (Heinle et al, 2010;Liu and Zhang, 2015;Kliangsuwan and Heednacram, 2015;Cheng and Yu, 2015) apply traditional features based on pixels. In this method, an all-sky image is treated as a collection of micro-structures just as a document consists of words, and it is represented by a high-dimensional histogram of micro-structures.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, texture features based on salient local binary patterns are applied for cloud classification, which achieves competitive performance (Liu et al, 2013;Liu and Zhang, 2015). Kliangsuwan and Heednacram (2015) proposed a new technique called fast Fourier transform projection on the x axis. This method extracts features by projecting logarithmic magnitude of fast Fourier transform coefficients of a cloud image on the x axis in frequency domain.…”

Section: Q LI Et Al: From Pixels To Patchesmentioning

confidence: 99%

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From pixels to patches: a cloud classification method based on a bag of micro-structures

Zhang

et al. 2016

Atmos. Meas. Tech.

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Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of microstructures mapped from image patches, rather than a collection of pixels. It represents the image with a weighted histogram of micro-structures. Based on this representation, BoMS recognizes the cloud class of the image by a support vector machine (SVM) classifier. Five classes of sky condition are identified: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness. BoMS is evaluated on a large data set, which contains 5000 all-sky images captured by a total-sky cloud imager located in Tibet (29.25 • N, 88.88 • E). BoMS achieves an accuracy of 90.9 % for 10-fold cross-validation, and it outperforms state-of-the-art methods with an increase of 19 %. Furthermore, influence of key parameters in BoMS is investigated to verify their robustness.

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Section: Discussionmentioning

confidence: 99%

Section: Q LI Et Al: From Pixels To Patchesmentioning

confidence: 99%

From pixels to patches: a cloud classification method based on a bag of micro-structures

Zhang

et al. 2016

Atmos. Meas. Tech.

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“…With the substantial amount of ground-based cloud images, ground-based cloud recognition has been extensively studied in the academic community in recent decades. Most traditional algorithms for ground-based cloud recognition utilize hand-crafted features, for example, brightness, texture, shape and color, to represent cloud images [12][13][14][15][16][17][18], but they are deficient in modeling complex data distribution. Recently, the convolutional neural network (CNN) [19][20][21][22][23][24] has achieved remarkable performance in various research fields due to the nature of learning highly nonlinear feature transformations.…”

Section: Introductionmentioning

confidence: 99%

Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition

et al. 2020

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In recent times, deep neural networks have drawn much attention in ground-based cloud recognition. Yet such kind of approaches simply center upon learning global features from visual information, which causes incomplete representations for ground-based clouds. In this paper, we propose a novel method named multi-evidence and multi-modal fusion network (MMFN) for ground-based cloud recognition, which could learn extended cloud information by fusing heterogeneous features in a unified framework. Namely, MMFN exploits multiple pieces of evidence, i.e., global and local visual features, from ground-based cloud images using the main network and the attentive network. In the attentive network, local visual features are extracted from attentive maps which are obtained by refining salient patterns from convolutional activation maps. Meanwhile, the multi-modal network in MMFN learns multi-modal features for ground-based cloud. To fully fuse the multi-modal and multi-evidence visual features, we design two fusion layers in MMFN to incorporate multi-modal features with global and local visual features, respectively. Furthermore, we release the first multi-modal ground-based cloud dataset named MGCD which not only contains the ground-based cloud images but also contains the multi-modal information corresponding to each cloud image. The MMFN is evaluated on MGCD and achieves a classification accuracy of 88.63% comparative to the state-of-the-art methods, which validates its effectiveness for ground-based cloud recognition.

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“…Kazantzidis et al (2012) proposed the use of a multicolor criterion on sky images, showing an average performance of about 87% using seven cloud categories. Kliangsuwan and Heednacram (2015) used a new methodology, based on the fast Fourier transform, for feature extraction for cloud classification. The overall accuracy of this methodology was shown to be 90% for the automatic classification of seven clouds types.…”

Section: Introductionmentioning

confidence: 99%

“…The method showed an accuracy of 90% for five classes of sky conditions. Regarding classification machine learning algorithms, the literature contains proposals ranging from artificial neural networks (Kliangsuwan & Heednacram, 2015;Lee et al, 1990;Singh & Glennen, 2005), to k-nearest neighbor (KNN) (Cheng & Yu, 2015;Heinle et al, 2010;Kazantzidis et al, 2012;Wacker et al, 2015) and support vector machines (SVM) (Schmidt et al, 2015;Taravat et al, 2015;Zhen et al, 2015). ANNs are a commonly machine learning technique used in cloud classification.…”

Section: Introductionmentioning

confidence: 99%

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

et al. 2017

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A methodology, aimed to be fully operational, for automatic cloud classification based on the synergetic use of a sky camera and a ceilometer is presented. The random forest machine learning algorithm was used to train the classifier with 19 input features: 12 extracted from the sky camera images and 7 from the ceilometer. The method was developed and tested based on a set of 717 images collected at the radiometric stations of the Univ. of Jaén (Spain). Up to nine different types of clouds (plus clear sky) were considered (clear sky, cumulus, stratocumulus, nimbostratus, altocumulus, altostratus, stratus, cirrocumulus, cirrostratus, and cirrus) plus an additional category multicloud, aiming to account for the frequent cases in which the sky is covered by several cloud types. A total of eight experiments was conducted by (1) excluding/including the ceilometer information, (2) including/excluding the multicloud category, and (3) using six or nine different cloud types, aside from the clear‐sky and multicloud category. The method provided accuracies ranging from 45% to 78%, being highly dependent on the use of the ceilometer information. This information showed to be particularly relevant for accurately classifying “cumuliform” clouds and to account for the multicloud category. In this regard, the camera information alone was found to be not suitable to deal with this category. Finally, while the use of the ceilometer provided an overall superior performance, some limitations were found, mainly related to the classification of clouds with similar cloud base height and geometric thickness.

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Feature extraction techniques for ground-based cloud type classification

Cited by 18 publications

References 22 publications

From pixels to patches: a cloud classification method based on a bag of micro-structures

From pixels to patches: a cloud classification method based on a bag of micro-structures

Multi-Evidence and Multi-Modal Fusion Network for Ground-Based Cloud Recognition

Automatic Cloud‐Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer

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