Anomaly Detection Algorithm Based on Subspace Local Density Estimation

Zhang, Chunkai; Yin, Ao

doi:10.4018/ijwsr.2019070103

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…Therefore, different threshold values were chosen to prevent overestimation or high underestimation of burnt areas. The threshold value is selected by analyzing extremely high values, for example, by the three-sigma algorithm [78], or Otsu's method [79]. After that, the validation of the fires obtained from the Sentinel-2 data is carried out, taking into account the limited available data due to cloud cover and the low regularity of Landsat satellite [78].…”

Section: Burned Area Detectionmentioning

confidence: 99%

“…The threshold value is selected by analyzing extremely high values, for example, by the three-sigma algorithm [78], or Otsu's method [79]. After that, the validation of the fires obtained from the Sentinel-2 data is carried out, taking into account the limited available data due to cloud cover and the low regularity of Landsat satellite [78]. Therefore, we proposed the following formula for partial verification of the fires detected by Sentinel-2 (the intersection of features from the Sentinel-2 and Landsat data):…”

Section: Burned Area Detectionmentioning

confidence: 99%

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Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

Yailymov,

Shelestov,

Yailymova

et al. 2023

Fire

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Wildfires cause extensive damage, but their rapid detection and cause assessment remains challenging. Existing methods utilize satellite data to map burned areas and meteorological data to model fire risk, but there are no information technologies to determine fire causes. It is crucially important in Ukraine to assess the losses caused by the military actions. This study proposes an integrated methodology and a novel framework integrating burned area mapping from Sentinel-2 data and fire risk modeling using the Fire Potential Index (FPI) in Google Earth Engine. The methodology enables efficient national-scale burned area detection and automated identification of anthropogenic fires in regions with low fire risk. Implemented over Ukraine, 104.229 ha were mapped as burned during July 2022, with fires inconsistently corresponding to high FPI risk, indicating predominantly anthropogenic causes.

show abstract

Section: Burned Area Detectionmentioning

confidence: 99%

Section: Burned Area Detectionmentioning

confidence: 99%

Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

Yailymov,

Shelestov,

Yailymova

et al. 2023

Fire

View full text Add to dashboard Cite

show abstract

“…While comparing the above three categories, distance-based approaches pro-duce binary outliers, density-based methods generate scored outliers and subspace-based techniques create both kinds of outliers, binary and scored. In paper[46], a local density estimator (variable sample technique) is implemented by using the T-Forest algorithm. It splits the data into subspaces and finally density of each instance determines score (outlierness).…”

mentioning

confidence: 99%

A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

Rehman

Khan

2021

ITC

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Recently, anomaly detection has acquired a realistic response from data mining scientists as a graph of its reputation has increased smoothly in various practical domains like product marketing, fraud detection, medical diagnosis, fault detection and so many other fields. High dimensional data subjected to outlier detection poses exceptional challenges for data mining experts and it is because of natural problems of the curse of dimensionality and resemblance of distant and adjoining points. Traditional algorithms and techniques were experimented on full feature space regarding outlier detection. Customary methodologies concentrate largely on low dimensional data and hence show ineffectiveness while discovering anomalies in a data set comprised of a high number of dimensions. It becomes a very difficult and tiresome job to dig out anomalies present in high dimensional data set when all subsets of projections need to be explored. All data points in high dimensional data behave like similar observations because of its intrinsic feature i.e., the distance between observations approaches to zero as the number of dimensions extends towards infinity. This research work proposes a novel technique that explores deviation among all data points and embeds its findings inside well established density-based techniques. This is a state of art technique as it gives a new breadth of research towards resolving inherent problems of high dimensional data where outliers reside within clusters having different densities. A high dimensional dataset from UCI Machine Learning Repository is chosen to test the proposed technique and then its results are compared with that of density-based techniques to evaluate its efficiency.

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Research and Implementation of Anomaly Detection Algorithm in Data Mining

Zhou

Liu

et al. 2021

Lecture Notes in Computer Science

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Anomaly Detection Algorithm Based on Subspace Local Density Estimation

Cited by 10 publications

References 24 publications

Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

Google Earth Engine Framework for Satellite Data-Driven Wildfire Monitoring in Ukraine

A Novel Density-based Technique for Outlier Detection of High Dimensional Data Utilizing Full Feature Space

Research and Implementation of Anomaly Detection Algorithm in Data Mining

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