Whirlwind prediction using cloud movement patterns on satellite image

Cahyanti, Rhoma; Hutama, Rendra Budi; Ramdlon, Rafi Haidar; Dwiastuti, Windasari; Hardiansyah, Fadilah Fahrul; Basuki, Achmad

doi:10.1109/kcic.2017.8228595

Cited by 7 publications

(8 citation statements)

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“…Clouds are forming some groups and located separately. In order to find the location of each group of clouds region-based segmentation was performed in previous research, Himawari 8 was used for predicting whirlwind [12]. In the segmentation process, they perform meng hee heng k-means algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…In 2017, Himawari 8 satellite image is used for predict whirlwind by Rhoma Cahyanti et al [12]. Inside the whole process of build the system, segmentation was performed using meng hee heng k-means.…”

Section: Originalitymentioning

confidence: 99%

“…This section explains the comparison result visualization for the previous algorithm used meng hee heng k-means and the proposed method with DBSCAN (Table 1). Meng hee heng k-means is commonly used for image segmentation in many field, just like the previous research which also implement it to segment himawari 8 satellite image [12]. Even though it is a common method for segmentation, but there is still another method that's worth to try and more suitable to the target.…”

Section: Experimental Comparisonmentioning

confidence: 99%

“…It Table 3 contains 74 rows consist of 74 clusters produces in one image for cumulonimbus cloud. Implement the previous clustering algorithm [12] using meng hee heng k-means into the same data, we obtain only 10 clusters. Figure 12 shows there are 10 different groups/clusters that are indicate by 10 different colors produced by meng hee heng k-means.…”

Section: Comparison Of Observations On Whirlwind Incidentsmentioning

confidence: 99%

See 3 more Smart Citations

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Sa'ada

Harsono

Basuki

2019

emitter

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Images contain a lot of information that can be used in a variety of areas. One of the images that have much information inside is satellite image. In order to extract the information properly, the image processing step should be performed properly. The segmentation process plays an important role in image processing, especially for feature extraction. Many ways were developed to perform the segmentation image. In this study, we apply DBSCAN clustering to segment images on whirlwind cloud feature extraction problems. DBSCAN is a density-based classifier method which means it is suitable to group a density-based data. While the image used in the segmentation process is the Himawari 8 satellite image which also contains density-based data. It contains various information about clouds condition like cloud type, cloud temperature, cloud humidity, rainfall potential based on cloud temperature, etc. This study uses Himawari 8 satellite images as input where the images taken are images several hours before a wirlwind event in an area, while the cluster method used is the DBSCAN algorithm. Clustering is done to get the extraction features of a wirlwind in the form of centroid points that characterize the movement of a cloud. Segmentation performance was observed based on the number of centroid points as a result of clustering several types of clouds in an area before a wirlwind occurred. Based on segmentation testing using the DBSCAN algorithm for cloud data in an area for several hours before a wirlwind, better segmentation performance was obtained compared to the segmentation results of the Meng hee heng k-means algorithm for the same test data specifications. DBSCAN separates a type of cloud in more detail that makes it easier to record each centroid of each cluster around the scene. It is even able to cluster small groups of clouds independently so that these small groups of clouds can also be detected as features.

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Section: Resultsmentioning

confidence: 99%

Section: Originalitymentioning

confidence: 99%

Section: Experimental Comparisonmentioning

confidence: 99%

Section: Comparison Of Observations On Whirlwind Incidentsmentioning

confidence: 99%

See 2 more Smart Citations

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Sa'ada

Harsono

Basuki

2019

emitter

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“…Calculating the position and movement of a beam centroid is a common requirement among many scientific disciplines and is a well-established practice. 1 Some wide-ranging applications of centroid measurement are star image analysis for star trackers and detectors, 2,3 photon beam localization, 4 tracking cloud movement, 5,6 and monitoring plant health. 7 In practice, centroid measurement is highly sensitive to the shape of the object being imaged, and the method must be tailored to the application.…”

Section: Introductionmentioning

confidence: 99%

Interpolation methods for tracking spacecraft in ultratight formation

Bradshaw

Gao

Homewood

2019

J. Astron. Telesc. Instrum. Syst.

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By measuring the centroid of a beam on a detector, one can track the movement of that beam across the detector. By tracking this movement, one can track the object encompassing the detector, for example, a spacecraft. A variety of system-specific performance inhibitors can make this a challenge, requiring a robust calibration method. The goal of this investigation is to model the true beam position of the instrument in terms of the measured beam position. For this, a mathematical model is created that interpolates and corrects the measured beam position using precollected position data-a "calibration model." The real-world scenario for this investigation is the flight-representative model of the fine lateral and longitudinal sensor (FLLS) instrument, built by Neptec Design Group and Neptec UK for the European Space Agency mission PROBA-3. Performance inhibitors for FLLS are cropping of the beam, imperfect optics, and a varying distance the beam has traveled (up to 250 m). Using bivariate spline interpolation for the FLLS calibration model gives the best performance, achieving a measurement accuracy well within the mission requirement of <300 μm.

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MeghNA: Cloud Detection and Motion Prediction in INSAT Images

Doshi¹,

Shah²,

Gadekar³

et al. 2021

Advances in Intelligent Systems and Computing

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Cloud detection is an important functionality of satellite-based remote sensing. Cloud motion prediction is essential for estimating the future positions of cloud masses. The ability to map cloud behavior is imperative to the successful operation of many on-ground endeavors. However, both processes encounter difficulties due to the nonlinearized phenomenon of cloud formation and deformation. Hence, satellite imagery-based approaches are more conducive to better research techniques and accuracy. In this paper, we propose a set of algorithms for cloud detection and nowcasting using INSAT satellite imagery. Approaches using mask RCNN and Kmeans clustering for cloud detection have been implemented and compared. Further, a convolutional LSTM model is proposed for cloud nowcasting that achieves a similarity index of 0.6942 with test images.

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Whirlwind prediction using cloud movement patterns on satellite image

Cited by 7 publications

References 1 publication

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Improvement of Segmentation Performance for Feature Extraction on Whirlwind Cloud-based Satellite Image using DBSCAN Clustering Algorithm

Interpolation methods for tracking spacecraft in ultratight formation

MeghNA: Cloud Detection and Motion Prediction in INSAT Images

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