Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model

Qu, Jiahui; Du, Qian; Li, Yunsong; Tian, Long; Xia, Haoming

doi:10.1109/tgrs.2020.3038722

Cited by 44 publications

(20 citation statements)

References 46 publications

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“…One is referred to BKG separation-before-anomaly detection, which separates BKG before anomaly detection takes place [43], [44]. Another is BKG removal-before-anomaly detection, which removes estimated BKG [45], [46] or reconstructed BKG via autoencoder (AE) or generative adversarial network (GAN) [47], [48], [49] prior to anomaly detection. Recently, a third approach is to use graph theory to better characterize anomalies from BKG in a rather robust manner [50], [51].…”

Section: Effective Anomaly Spacementioning

confidence: 99%

Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection

Chang

2022

IEEE Trans. Geosci. Remote Sensing

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Section: Effective Anomaly Spacementioning

confidence: 99%

Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection

Chang

2022

IEEE Trans. Geosci. Remote Sensing

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“…If any kind of anomaly is detected, it will give an alert to the system. Gaussian Mixture Model (GMM) [20] is used for object detection in this framework. The main components that are used in this method are preprocessing phase, feature extraction phase, and recognition phase.…”

Section: Multiple Anomalous Activity Detectionmentioning

confidence: 99%

State-of-the-Art Violence Detection Techniques: A review

Biswas

Jibon

Kabir

et al. 2022

AJRCoS

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Surveillance systems are playing a significant role in law enforcement and city safety. It is important to detect violent and suspicious behaviors automatically in video surveillance scenarios, for instance, railway stations, schools, hospitals to avoid any casualties which could cause social, economic, and ecological damage. Automatic detection of violence for quick actions is very significant and can efficiently help law enforcement departments. So, researchers are doing a lot of research on different techniques for detecting violence. This research study reviews various techniques and methods for detecting violent or anomalous activities from surveillance video that have been proposed by many researchers in recent years. The method of detection is divided into three categories. These categories are based on the classification techniques used. These categories are: traditional violence detection using machine learning, Support Vector Machine (SVM) & Deep Learning. Feature extraction & Object detection techniques are also described for each category. Moreover, dataset & video features that help in the recognition process are also discussed. The overall research finding has been discussed which will help the researcher in their future work in this field.

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“…This threshold value either knowledge-based or not adaptive to changing WSN environmental conditions. The novel contribution to vampire node detection follows the unsupervised anomaly detection, [21][22][23] which is predominantly used in computer vision and first time introduced in vampire attack detection. The Gaussian mixture model (GMM) is used to find the vampire node based on the connected nodes' anomalous co-operative trust values.…”

Section: Cooperative Trust Calculation and Vampire Node Detectionmentioning

confidence: 99%

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

Juneja

Dinkar

Gupta

2021

Concurrency and Computation

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Sensor nodes in WSN play a vital role in communication, IoTs and many other emergencies too. However, the energy consumption of nodes is a major setback to these, which incites various malicious nodes/attacks. This article studies and presents the solution to Vampire attack-one of those kinds of attacks. It depletes the energy by route elongation of data transmission. This article has suggested a novel two-fold mechanism to detect the attack by integrating co-operation trust mechanism and the mitigation of the attack by selecting the secure route by policy gradient-deep reinforcement learning. The designed protocol also ensures the selection of a secure hop even in the presence of the vampire attack. The results are compared with various other existing state-of-the-art schemes and have improved the detection ratio by 20% compared to the forecasting methods applied for detecting the vampire node's behavior. The network lifetime has also improved by 3% than the benchmark dynamic source routing.

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Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model

Cited by 44 publications

References 46 publications

Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection

Target-to-Anomaly Conversion for Hyperspectral Anomaly Detection

State-of-the-Art Violence Detection Techniques: A review

An anomalous co‐operative trust & PG‐DRL based vampire attack detection & routing

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