scholarly journals An Improved Cloud Detection Method for GF-4 Imagery

2020 ◽  
Vol 12 (9) ◽  
pp. 1525
Author(s):  
Ming Lu ◽  
Feng Li ◽  
Bangcheng Zhan ◽  
He Li ◽  
Xue Yang ◽  
...  
Keyword(s):  
Real Time ◽  
Detection Methods ◽  
Support Vector ◽  
Optical Remote Sensing ◽  
Cloud Detection ◽  
Dynamic Features ◽  
Improve Image Quality ◽  
Clear Sky ◽  
Important Means ◽  
The Difference

Clouds are significant barriers to the application of optical remote sensing images. Accurate cloud detection can help to remove contaminated pixels and improve image quality. Many cloud detection methods have been developed. However, traditional methods either rely heavily on thermal infrared bands or clear-sky images. When traditional cloud detection methods are used with Gaofen 4 (GF-4) imagery, it is very difficult to separate objects with similar spectra, such as ice, snow, and bright sand, from clouds. In this paper, we propose a new method, named Real-Time-Difference (RTD), to detect clouds using a pair of images obtained by the GF-4 satellite. The RTD method has four main steps: (1) data preprocessing, including transforming digital value (DN) to Top of Atmosphere (TOA) reflectance, and orthographic and geometric correction; (2) the computation of a series of cloud indexes for a single image to highlight clouds; (3) the calculation of the difference between a pair of real-time images in order to obtain moved clouds; and (4) confirming the clouds and background by analyzing their physical and dynamic features. The RTD method was validated in three sites located in the Hainan, Liaoning, and Xinjiang areas of China. The results were compared with those of a popular classifier, Support Vector Machine (SVM). The results showed that RTD outperformed SVM; for the Hainan, Liaoning, and Xinjiang areas, respectively, the overall accuracy of RTD reached 95.9%, 94.1%, and 93.9%, and its Kappa coefficient reached 0.92, 0.88, and 0.88. In the future, we expect RTD to be developed into an important means for the rapid detection of clouds that can be used on images from geostationary orbit satellites.

scholarly journals Dynamic Deformation Reconstruction of Variable Section WING with Fiber Bragg Grating Sensors

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3350 ◽  
Author(s):  
Zhen Fu ◽  
Yong Zhao ◽  
Hong Bao ◽  
Feifei Zhao
Keyword(s):  
Finite Element ◽  
Fiber Bragg Grating ◽  
Real Time ◽  
Beam Theory ◽  
Reconstruction Algorithm ◽  
Support Vector ◽  
Bragg Grating ◽  
Variable Section ◽  
The Difference ◽  
Fuzzy Network

In order to monitor the variable-section wing deformation in real-time, this paper proposes a dynamic reconstruction algorithm based on the inverse finite element method and fuzzy network to sense the deformation of the variable-section beam structure. Firstly, based on Timoshenko beam theory and inverse finite element framework, a deformation reconstruction model of variable-section beam element was established. Then, considering the installation error of the fiber Bragg grating (FBG) sensor and the dynamic un-modeled error caused by the difference between the static model and dynamic model, the real-time measured strain was corrected using a solidified fuzzy network. The parameters of the fuzzy network were learned using support vector machines to enhance the generalization ability of the fuzzy network. The loading deformation experiment shows that the deformation of the variable section wing can be reconstructed with the proposed algorithm in high precision.

scholarly journals Dynamic Features Based on Flow-Correlation and HOG for Recognition of Discrete Facial Expressions

2020 ◽  
Vol 34 (5) ◽  
pp. 585-594
Author(s):  
Shivangi Anthwal ◽  
Dinesh Ganotra
Keyword(s):  
Interpersonal Communication ◽  
Facial Expressions ◽  
Visual Information ◽  
Human Cognition ◽  
Support Vector ◽  
Dynamic Features ◽  
Difference Image ◽  
Neutral Face ◽  
The Difference ◽  
Emotional Face

Facial expressions are the most preeminent means of conveying one’s emotions and play a significant role in interpersonal communication. Researchers are in pursuit of endowing machines with the ability to interpret emotions from facial expressions as that will make human-computer interaction more efficient. With the objective of effective affect cognition from visual information, we present two dynamic descriptors that can recognise seven principal emotions. The variables of the appearance-based descriptor, FlowCorr, indicate intra-class similarity and inter-class difference by quantifying the degree of correlation of optical flow associated with the image pair and each pre-designed template describing the motion pattern associated with different expressions. The second shape-based descriptor, dyn-HOG, finds the HOG values of the difference image derived by subtracting neutral face from emotional face, and is demonstrated to be more discriminative than previously used static HOG descriptors for classifying facial expressions. Recognition accuracies with multi-class support vector machine obtained on the CK+ and KDEF-dyn datasets are competent with the results of state-of-the-art techniques and empirical analysis of human cognition of emotions.

scholarly journals A method for cloud detection and opacity classification based on ground based sky imagery

2012 ◽  
Vol 5 (11) ◽  
pp. 2881-2892 ◽  
Author(s):  
M. S. Ghonima ◽  
B. Urquhart ◽  
C. W. Chow ◽  
J. E. Shields ◽  
A. Cazorla ◽  
...  
Keyword(s):  
Training Image ◽  
Classification Model ◽  
Cloud Detection ◽  
New Classification ◽  
Short Term ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Set ◽  
The Difference ◽  
Solar Power Forecasting

Abstract. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

scholarly journals A method for cloud detection and opacity classification based on ground based sky imagery

2012 ◽  
Vol 5 (4) ◽  
pp. 4535-4569 ◽  
Author(s):  
M. S. Ghonima ◽  
B. Urquhart ◽  
C. W. Chow ◽  
J. E. Shields ◽  
A. Cazorla ◽  
...  
Keyword(s):  
Training Image ◽  
Classification Model ◽  
Cloud Detection ◽  
New Classification ◽  
Short Term ◽  
Clear Sky ◽  
Cloud Classification ◽  
Image Set ◽  
The Difference ◽  
Solar Power Forecasting

Abstract. Digital images of the sky obtained using a total sky imager (TSI) are classified pixel by pixel into clear sky, optically thin and optically thick clouds. A new classification algorithm was developed that compares the pixel red-blue ratio (RBR) to the RBR of a clear sky library (CSL) generated from images captured on clear days. The difference, rather than the ratio, between pixel RBR and CSL RBR resulted in more accurate cloud classification. High correlation between TSI image RBR and aerosol optical depth (AOD) measured by an AERONET photometer was observed and motivated the addition of a haze correction factor (HCF) to the classification model to account for variations in AOD. Thresholds for clear and thick clouds were chosen based on a training image set and validated with set of manually annotated images. Misclassifications of clear and thick clouds into the opposite category were less than 1%. Thin clouds were classified with an accuracy of 60%. Accurate cloud detection and opacity classification techniques will improve the accuracy of short-term solar power forecasting.

scholarly journals A RGB channel operation for removal of the difference of atmospheric scattering and its application on total sky cloud detection

2016 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Qualitative Assessment ◽  
The Sun ◽  
Cloud Detection ◽  
Recognition Error ◽  
Atmospheric Scattering ◽  
Clear Sky ◽  
Different Types ◽  
Rgb Images ◽  
The Difference

Abstract. The inhomogeneous sky background presents a great challenge for accurate cloud recognition from the total sky images. A channel operation was introduced in this study to produce a new composite channel in which the difference of atmospheric scattering has been removed and a homogeneous sky background can be obtained. Following this, a new cloud detection algorithm was proposed, which combined the merits of the differencing and threshold methods and named "differencing and threshold combination algorithm (DTCA)". Firstly, the channel operation was applied to transform 3-D RGB images to the new channel, then the circumsolar saturated pixels and its circularity were used to judge whether the sun is visible or not in the image. When the sun is obscured, a single threshold can be used to identify cloud pixels, and, when the sun is visible in the image, the true clear sky background differencing algorithm is adopted to detect clouds. The qualitative assessment for eight different total sky images shows the DTCA algorithm obtained satisfactory cloud identification effectiveness for thin clouds and in the circumsolar and near-horizon regions. Quantitative evaluation also shows the DTCA algorithm achieved the highest cloud recognition precision for five different types of clouds, with an average recognition error rate of 8.7 %.

scholarly journals A total sky cloud detection method using real clear sky background

2015 ◽  
Vol 8 (12) ◽  
pp. 13073-13098 ◽  
Author(s):  
J. Yang ◽  
Q. Min ◽  
W. Lu ◽  
Y. Ma ◽  
W. Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show, as long as the positions of the sun in the two images are the same, the cloud detection results are satisfactory. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals A total sky cloud detection method using real clear sky background

2016 ◽  
Vol 9 (2) ◽  
pp. 587-597 ◽  
Author(s):  
Jun Yang ◽  
Qilong Min ◽  
Weitao Lu ◽  
Ying Ma ◽  
Wen Yao ◽  
...  
Keyword(s):  
Detection Algorithm ◽  
Brightness Distribution ◽  
Detection Methods ◽  
Cloud Detection ◽  
Image Brightness ◽  
Clear Sky ◽  
Sensitivity Tests ◽  
Thin Cloud ◽  
Distribution Sensitivity ◽  
Solar Position

Abstract. The brightness distribution of sky background is usually non-uniform, which creates many problems for traditional cloud detection methods, including the failure of thin cloud detection in total sky images and significantly reducing retrieval accuracy in the circumsolar and near-horizon regions. This paper describes the development of a new cloud detection algorithm, named "clear sky background differencing (CSBD)", which is accomplished by differencing the original image and the corresponding clear sky background image using the images' green channel. First, a library of clear sky background images with a variety of solar elevation angles needs to be developed. The image rotation and image brightness adjustment algorithms are applied to ensure the two images being differenced have the same solar position and similar brightness distribution. Sensitivity tests show that the cloud detection results are satisfactory when the two images have the same solar positions. Several experimental cases show that the CSBD algorithm obtains good cloud recognition results visually, especially for thin clouds.

scholarly journals Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

2017 ◽  
Vol 10 (1) ◽  
pp. 199-208 ◽  
Author(s):  
Hsu-Yung Cheng ◽  
Chih-Lung Lin
Keyword(s):  
Supervised Learning ◽  
Detection Methods ◽  
Support Vector ◽  
Detection Accuracy ◽  
Cloud Detection ◽  
Multi Scale ◽  
Cloud Models ◽  
Learning Techniques ◽  
Image Patches ◽  
Local Image

Abstract. Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

scholarly journals A kernel-driven BRDF model to inform satellite-derived visible anvil cloud detection

2020 ◽  
Vol 13 (10) ◽  
pp. 5491-5511
Author(s):  
Benjamin R. Scarino ◽  
Kristopher Bedka ◽  
Rajendra Bhatt ◽  
Konstantin Khlopenkov ◽  
David R. Doelling ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Deep Convection ◽  
Detection Methods ◽  
Cloud Detection ◽  
Bidirectional Reflectance ◽  
Cloud Optical Depth ◽  
Convective Clouds ◽  
Clear Sky ◽  
Almost All ◽  
Bidirectional Reflectance Distribution

Abstract. Satellites routinely observe deep convective clouds across the world. The cirrus outflow from deep convection, commonly referred to as anvil cloud, has a ubiquitous appearance in visible and infrared (IR) wavelength imagery. Anvil clouds appear as broad areas of highly reflective and cold pixels relative to the darker and warmer clear sky background, often with embedded textured and colder pixels that indicate updrafts and gravity waves. These characteristics would suggest that creating automated anvil cloud detection products useful for weather forecasting and research should be straightforward, yet in practice such product development can be challenging. Some anvil detection methods have used reflectance or temperature thresholding, but anvil reflectance varies significantly throughout a day as a function of combined solar illumination and satellite viewing geometry, and anvil cloud top temperature varies as a function of convective equilibrium level and tropopause height. This paper highlights a technique for facilitating anvil cloud detection based on visible observations that relies on comparative analysis with expected cloud reflectance for a given set of angles, thereby addressing limitations of previous methods. A 1-year database of anvil-identified pixels, as determined from IR observations, from several geostationary satellites was used to construct a bidirectional reflectance distribution function (BRDF) model to quantify typical anvil reflectance across almost all expected viewing, solar, and azimuth angle configurations, in addition to the reflectance uncertainty for each angular bin. Application of the BRDF model for cloud optical depth retrieval in deep convection is described as well.

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