A Cloud Detection Algorithm for MODIS Images Combining Kmeans Clustering and Otsu Method

Xiang, Peisu

doi:10.1088/1757-899x/392/6/062199

Cited by 9 publications

(7 citation statements)

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“…This method iterated through all possible threshold values and chose one that minimizes the sum of the foreground and background variance (Otsu 1979). Although the Otsu technique works best for images with bimodal intensity peaks, it has been successfully used previously to distinguish terrestrial cloud features in sky images by Xiang (2018) and Yang et al (2012).…”

Section: Power Spectra Of the Cloud Featuresmentioning

confidence: 99%

Geometric Properties of Water-ice Clouds as Observed from Jezero Crater in the First 600 sols with the NavCam Instrument On Board the Mars2020 Rover, Perseverance

Patel,

Tamppari,

de la Torre Juárez

et al. 2023

Planet. Sci. J.

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In the first 600 sols of the Mars2020 mission, LS 5.6o – 316.8o, 46 cloud movies and 145 cloud surveys were collected to observe clouds at Jezero Crater, the landing site of the Perseverance Rover. Cloud movies were processed using the Mean-Frame Subtraction (MFS) method for revealing cloud structures, which were subsequently analyzed using digital-image processing. Two-dimensional Fast Fourier Transforms (2D-FFT) were used to compute cloud structure sizes ranging from 2.90 to 15.25 km for clouds between 30 and 50 km altitude, based on coincident Mars Climate Sounder vertical profiles of atmospheric water-ice. Same-value thresholding was used to detect the cloud structures in MFS-processed and projected cloud movies. The resolution dependence that was needed to resolve these structures over various thresholds was examined to find multifractal scaling of Mars clouds for resolutions between 0.1 and 1.6 km. We characterize the multiscaling observed in the images and its implications for the design of cloud-tracking cameras from the surface as well as for cloud-resolving models.

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Section: Power Spectra Of the Cloud Featuresmentioning

confidence: 99%

Geometric Properties of Water-ice Clouds as Observed from Jezero Crater in the First 600 sols with the NavCam Instrument On Board the Mars2020 Rover, Perseverance

Patel,

Tamppari,

de la Torre Juárez

et al. 2023

Planet. Sci. J.

View full text Add to dashboard Cite

show abstract

“…Since the K-means algorithm always converges, K-means can always reach a steady state in a finite number of steps, i.e., the clustering centres will not change again [14][15][16]. Since changes in the clustering centres often occur in the course of the previous iterations, in order to optimise the time complexity of the algorithm, the iterative process can usually be stopped and the results can be output directly when only more than 99% of the data points belong to clusters that no longer change.…”

Section: Clustering Analysis Of Student Behaviour Using the K-means A...mentioning

confidence: 99%

Challenges and Strategies of Student Management in Universities in the Context of Big Data

Zhang

2022

Mobile Information Systems

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As the most active and knowledge-intensive university, the forefront of the use of network information technology, the education, management, and service mode of the school, and the ideology, learning methods, and behavioural habits of teachers and students will be profoundly affected by the era of big data. The current form of student management in universities is too old and needs to be replaced by a new management system. With the promotion of the construction of smart campus, the rapid development of big data technology has realised the innovation of student management in colleges and universities. This paper takes students of Wuhan University as the research object. By collecting various application data in the campus information system, the K-means algorithm in cluster analysis is used to classify students’ campus behavioural characteristics, and then the Apriori algorithm is used to correlate students’ behavioural characteristics with their academic performance. The experimental results show that there is a close relationship between the consumption behaviour, work and rest behaviour, study behaviour characteristics of different student groups, and their academic performance. Using the results of these analyses, universities can adopt differentiated management measures for different categories of students, which can help improve students’ academic performance as well as further enhance the efficiency of student management.

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“…They used infrared bands' dust storms' brightness temperature sampling results, the tests, and their thresholds to identify dust from cloud and clear-sky areas. Authors of [29] proposed a method to choose the initial clustering center for the k-means algorithm and then optimal threshold detection using the Otsu method is used on MODIS data to determine cloud/ no cloud pixel.…”

Section: Cloud/no Cloudmentioning

confidence: 99%

Cloud detection methodologies: variants and development—a review

Mahajan

Fataniya

2019

Complex Intell. Syst.

125

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Cloud detection is an essential and important process in satellite remote sensing. Researchers proposed various methods for cloud detection. This paper reviews recent literature (2004-2018) on cloud detection. Literature reported various techniques to detect the cloud using remote-sensing satellite imagery. Researchers explored various forms of Cloud detection like Cloud/ No cloud, Snow/Cloud, and Thin Cloud/Thick Cloud using various approaches of machine learning and classical algorithms. Machine learning methods learn from training data and classical algorithm approaches are implemented using a threshold of different image parameters. Threshold-based methods have poor universality as the values change as per the location. Validation on ground-based estimates is not included in many models. The hybrid approach using machine learning, physical parameter retrieval, and ground-based validation is recommended for model improvement.

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A Cloud Detection Algorithm for MODIS Images Combining Kmeans Clustering and Otsu Method

Cited by 9 publications

References 0 publications

Geometric Properties of Water-ice Clouds as Observed from Jezero Crater in the First 600 sols with the NavCam Instrument On Board the Mars2020 Rover, Perseverance

Geometric Properties of Water-ice Clouds as Observed from Jezero Crater in the First 600 sols with the NavCam Instrument On Board the Mars2020 Rover, Perseverance

Challenges and Strategies of Student Management in Universities in the Context of Big Data

Cloud detection methodologies: variants and development—a review

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