scholarly journals Daytime Cloud Detection Method Using the All-Sky Imager over PERMATApintar Observatory

Universe ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 41
Author(s):  
Mohammad Afiq Dzuan Mohd Azhar ◽  
Nurul Shazana Abdul Hamid ◽  
Wan Mohd Aimran Wan Mohd Kamil ◽  
Nor Sakinah Mohamad
Keyword(s):  
Detection Method ◽  
Color Difference ◽  
Database System ◽  
New Method ◽  
Cloud Detection ◽  
Cloud Coverage ◽  
B Method ◽  
Difference Methods ◽  
Sky Imager ◽  
Sky Conditions

In this study, we explored a new method of cloud detection called the Blue-Green (B-G) Color Difference, which is adapted from the widely used Red-Blue (R-B) Color Difference. The objective of this study was to test the effectiveness of these two methods in detecting daytime clouds. Three all-sky images were selected from a database system at PERMATApintar Observatory. Each selected all-sky image represented different sky conditions, namely clear, partially cloudy and overcast. Both methods were applied to all three images and compared in terms of cloud coverage detection. Our analysis revealed that both color difference methods were able to detect a thick cloud efficiently. However, the B-G was able to detect thin clouds better compared to the R-B method, resulting in a higher and more accurate cloud coverage detection.

scholarly journals The Evaluation of a New Method to Detect Mixing Layer Heights Using Lidar Observations

2015 ◽  
Vol 32 (11) ◽  
pp. 2041-2051 ◽  
Author(s):  
Micheal Hicks ◽  
Ricardo Sakai ◽  
Everette Joseph
Keyword(s):  
Hybrid Method ◽  
Detection Method ◽  
Error Function ◽  
Mixing Layer ◽  
New Method ◽  
Mixing Height ◽  
Critical Components ◽  
Sky Conditions ◽  
Aerosol Backscatter ◽  
Lidar Observations

AbstractA new automatic mixing layer height detection method for lidar observations of aerosol backscatter profiles is presented and evaluated for robustness. The new detection method incorporates the strengths of Steyn et al.’s error function–ideal profile (ERF) method and Davis et al.’s wavelet covariance transform (WCT) method. These two methods are critical components of the new method, and their robustness is also evaluated and then contrasted to the new method. The new method is applied to aerosol backscatter observations in two ways: 1) by looking for the most realistic mixing height throughout the entire profile and 2) by searching for mixing height below significant elevated obscurations (e.g., clouds or aerosol layers). The first approach is referred to as the hybrid method and the second as the hybrid-lowest method. Coincident radiosounding observations of mixing heights are used to independently reference the lidar-based estimates.There were 4030 cases examined over a 5-yr period for mixing heights. The efficacy of the lidar-based methods was determined based on diurnal, seasonal, stability, and sky obscuration conditions. Of these conditions, the hybrid method performed best for unstable and cloudy situations. It determined mixing heights reliably (less than ±0.30-km bias) for close to 70% of those cases. The hybrid-lowest method performed best in stable and clear-sky conditions; it determined mixing heights reliably for over 70% of those cases. The WCT method performed the best overall.

scholarly journals Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

2021 ◽  
Vol 13 (18) ◽  
pp. 3646
Author(s):  
Zhiwen Wu ◽  
Juan Li ◽  
Zhengkun Qin
Keyword(s):  
Standard Deviation ◽  
Satellite Data ◽  
Detection Method ◽  
Probability Of Detection ◽  
New Method ◽  
Spatial And Temporal Variability ◽  
Forecast Errors ◽  
Cloud Detection ◽  
Factors Affecting ◽  
Response Characteristics

Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.

scholarly journals A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1197-1201
Author(s):  
L. L. Jia ◽  
X. Q. Wang
Keyword(s):  
Detection Method ◽  
Texture Feature ◽  
New Method ◽  
Detection Methods ◽  
Cloud Detection ◽  
Segmentation Method ◽  
Optical Images ◽  
Routine Work ◽  
Cloud Mask ◽  
Better Than

Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90 %. The proposed method can meet the needs of routine work.

scholarly journals Seasonal cycle of cloud cover analyzed using Meteosat images

1998 ◽  
Vol 16 (3) ◽  
pp. 331-341 ◽  
Author(s):  
J. Massons ◽  
D. Domingo ◽  
J. Lorente
Keyword(s):  
Spatial Resolution ◽  
Seasonal Cycle ◽  
Cloud Cover ◽  
High Spatial Resolution ◽  
Detection Method ◽  
Cloud Amount ◽  
Cloud Detection ◽  
Total Cloud Amount ◽  
Cloud Parameters ◽  
Cloud Properties

Abstract. A cloud-detection method was used to retrieve cloudy pixels from Meteosat images. High spatial resolution (one pixel), monthly averaged cloud-cover distribution was obtained for a 1-year period. The seasonal cycle of cloud amount was analyzed. Cloud parameters obtained include the total cloud amount and the percentage of occurrence of clouds at three altitudes. Hourly variations of cloud cover are also analyzed. Cloud properties determined are coherent with those obtained in previous studies.Key words. Cloud cover · Meteosat

Cloud Detection Method Based on Spectral Area Ratios in MODIS Data

2015 ◽  
Vol 41 (6) ◽  
pp. 561-576
Author(s):  
Feng Guo ◽  
Xiaohua Shen ◽  
Lejun Zou ◽  
Yupeng Ren ◽  
Yi Qin ◽  
...  
Keyword(s):  
Detection Method ◽  
Cloud Detection ◽  
Modis Data ◽  
Spectral Area

Height-dependent meteor temperatures and comparisons with lidar and OH measurements

2007 ◽  
Vol 85 (2) ◽  
pp. 173-187 ◽  
Author(s):  
W K Hocking ◽  
P S Argall ◽  
R P Lowe ◽  
R J Sica ◽  
H Ellinor
Keyword(s):  
Good Accuracy ◽  
Sufficient Accuracy ◽  
New Method ◽  
Precise Knowledge ◽  
Rayleigh Lidar ◽  
Sky Conditions

A new method is introduced that allows meteor radars to potentially produce height-dependent temperatures, rather than simply averages over the meteor region. The method is applied to data from the Clovar radar, near London, Ontario, and then a three-way comparison between Rayleigh lidar temperatures, hydroxyl temperatures, and meteor temperatures is undertaken. The three methods prove to be complementary. The OH measurements have good accuracy, but suffer slightly from lack of precise knowledge about their height and the fact that they are effectively integrated over the depth of the OH layer. The lidar temperatures are measured at well-defined altitudes and have better accuracy than the meteor method. The meteor temperatures have the largest errors, but still provide sufficient accuracy for many types of atmospheric studies, and have the advantage that these measurements can be made 24 h a day and in all sky conditions (including during cloud and strong sunlight and moonlight). The measurements from these instruments are complementary in that they are useful for studying the temperature on different time and altitude scales. PACS No.: 94.10.Dy

scholarly journals EFFICIENT LINE DETECTION METHOD BASED ON 2D CONVOLUTION FILTER

2021 ◽  
Vol 11 (4) ◽  
pp. 22-27
Author(s):  
Paweł Kowalski ◽  
Piotr Tojza
Keyword(s):  
Detection Method ◽  
Computational Cost ◽  
New Method ◽  
Line Detection ◽  
Curve Detection ◽  
Real Time Image ◽  
Convolution Filter ◽  
Straight Lines ◽  
Time Image ◽  
Local Detection

The article proposes an efficient line detection method using a 2D convolution filter. The proposed method was compared with the Hough transform, the most popular method of straight lines detection. The developed method is suitable for local detection of straight lines with a slope from -45˚ to 45˚.  Also, it can be used for curve detection which shape is approximated with the short straight sections. The new method is characterized by a constant computational cost regardless of the number of set pixels. The convolution is performed using the logical conjunction and sum operations. Moreover, design of the developed filter and the method of filtration allows for parallelization. Due to constant computation cost, the new method is suitable for implementation in the hardware structure of real-time image processing systems.

scholarly journals New Edge Detection Method for Indonesian Batik

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Adi Purnomo Shidi ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Analysis Method ◽  
Unique Pattern ◽  
Detection Analysis ◽  
Edge Detection Method

Abstrak. Metode Baru Deteksi Tepi untuk Batik Indonesia. Didalam paper ini, diusulkan sebuah metode pendeteksi baru untuk motif batik. Deteksi tepi sudah sangat sering digunakan didalam pemrosesan gambar. Batik motif adalah salah satu contoh gambar yang memiliki bentuk yang unik dan menarik untuk dianalisis. Metode yang digunakan pada paper ini adalam metode canny dan prewit dan akan menghasilkan metode baru yaitu metode Thomas. Perbedaan antara metode dan hasil akan dilihat dari sisi ketepatan, qualitas hasil dan kejelasan. Contoh batik yang akan digunakan adalah motif parang, motife lereng dan udan liris. Ketiga batik tersebut memiliki pola  yang unik. Kata kunci : Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris. Abstract. New Edge Detection Method for Indonesian Batik. In this paper, we propose a new edge detection analysis method on batiks motif. Edge detection has been oftenly  used in computer vision and image processing. Indonesian  Batiks motif are some example of graphic picture that has unique pattern that interesting to analyse. The method that used for example on this paper are canny and prewit and produce a new method, thomas method. the different  amongs the method, the result of comparison appears on quality, accuracy and clarity. The example that we use are parang batiks motive, lereng batiks motive, and udan liris batiks motive. Three of batiks motive above are have unique pattern. Keywords: Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris.

Improving the Quality Assessment band in Landsat cloud images for the application of cloud removal  

2021 ◽  
Author(s):  
Boli Yang ◽  
Yan Feng ◽  
Ruyin Cao
Keyword(s):  
Quality Assessment ◽  
Land Surface ◽  
Missing Values ◽  
Similarity Index ◽  
Visual Assessment ◽  
New Method ◽  
Landsat Data ◽  
Cloud Detection ◽  
Cloud Removal ◽  
Ultimate Objective

<p>Cloud contamination is a serious obstacle for the application of Landsat data. Thick clouds can completely block land surface information and lead to missing values. The reconstruction of missing values in a Landsat cloud image requires the cloud and cloud shadow mask. In this study, we raised the issue that the quality of the quality assessment (QA) band in current Landsat products cannot meet the requirement of thick-cloud removal. To address this issue, we developed a new method (called Auto-PCP) to preprocess the original QA band, with the ultimate objective to improve the performance of cloud removal on Landsat cloud images. We tested the new method at four test sites and compared cloud-removed images generated by using three different QA bands, including the original QA band, the modified QA band by a dilation of two pixels around cloud and cloud shadow edges, and the QA band processed by Auto-PCP (“QA_Auto-PCP”). Experimental results, from both actual and simulated Landsat cloud images, show that QA_Auto-PCP achieved the best visual assessment for the cloud-removed images, and had the smallest RMSE values and the largest Structure SIMilarity index (SSIM) values. The improvement for the performance of cloud removal by QA_Auto-PCP is because the new method substantially decreases omission errors of clouds and shadows in the original QA band, but meanwhile does not increase commission errors. Moreover, Auto-PCP is easy to implement and uses the same data as cloud removal without additional image collections. We expect that Auto-PCP can further popularize cloud removal and advance the application of Landsat data.     </p><p><strong> </strong></p><p><strong>Keywords: </strong>Cloud detection, Cloud shadows, Cloud simulation, Cloud removal, MODTRAN</p>

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