Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data

Hytla, Patrick C.; Hardie, Russell C.; Eismann, Michael T.; Meola, Joseph

doi:10.1117/1.3236689

Cited by 26 publications

(8 citation statements)

References 2 publications

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“…It computes the pixel’s Mahalanobis distance to the component mean. Fuzzy Cluster-Based Anomaly Detection (FCBAD) [ 15 ] is a novel extension of the CBAD and GMRX algorithms. It assumes that each pixel can have several possible fuzzy logic membership functions in fuzzy c-means clustering and computes each pixel’s Mahalanobis distance to the component mean.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers regard the Reed–Xiaoli (RX) [ 10 ] detector as the benchmark for hyperspectral anomaly detection due to its simple principle, low computational complexity, and relatively good performance. We thoroughly investigated several hyperspectral anomaly detection methods, such as the RX [ 10 ], the Kernel-RX Algorithm (KRX) [ 11 ], the Gaussian Mixture RX Anomaly Detector (GMRX) [ 12 ], the Complementary Subspace Detector (CSD) [ 13 ], Cluster-Based Anomaly Detection (CBAD) [ 14 ], Fuzzy Cluster-Based Anomaly Detection (FCBAD) [ 15 ], HS-AD with Attribute and Edge-Preserving Filters (AED) [ 16 ], HS-AD with Kernel iForest (KIFD) [ 17 ], and Local Summation Unsupervised Nearest Regularized Subspace with an Outlier Removal Anomaly Detector (LSUNRSORAD) [ 18 ]. We found that each method’s performance depends upon specific background characteristic constraints and is effective only in some scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Greedy Ensemble Hyperspectral Anomaly Detection

Hossain,

Younis,

Robinson

et al. 2024

J. Imaging

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Hyperspectral images include information from a wide range of spectral bands deemed valuable for computer vision applications in various domains such as agriculture, surveillance, and reconnaissance. Anomaly detection in hyperspectral images has proven to be a crucial component of change and abnormality identification, enabling improved decision-making across various applications. These abnormalities/anomalies can be detected using background estimation techniques that do not require the prior knowledge of outliers. However, each hyperspectral anomaly detection (HS-AD) algorithm models the background differently. These different assumptions may fail to consider all the background constraints in various scenarios. We have developed a new approach called Greedy Ensemble Anomaly Detection (GE-AD) to address this shortcoming. It includes a greedy search algorithm to systematically determine the suitable base models from HS-AD algorithms and hyperspectral unmixing for the first stage of a stacking ensemble and employs a supervised classifier in the second stage of a stacking ensemble. It helps researchers with limited knowledge of the suitability of the HS-AD algorithms for the application scenarios to select the best methods automatically. Our evaluation shows that the proposed method achieves a higher average F1-macro score with statistical significance compared to the other individual methods used in the ensemble. This is validated on multiple datasets, including the Airport–Beach–Urban (ABU) dataset, the San Diego dataset, the Salinas dataset, the Hydice Urban dataset, and the Arizona dataset. The evaluation using the airport scenes from the ABU dataset shows that GE-AD achieves a 14.97% higher average F1-macro score than our previous method (HUE-AD), at least 17.19% higher than the individual methods used in the ensemble, and at least 28.53% higher than the other state-of-the-art ensemble anomaly detection algorithms. As using the combination of greedy algorithm and stacking ensemble to automatically select suitable base models and associated weights have not been widely explored in hyperspectral anomaly detection, we believe that our work will expand the knowledge in this research area and contribute to the wider application of this approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Greedy Ensemble Hyperspectral Anomaly Detection

Hossain,

Younis,

Robinson

et al. 2024

J. Imaging

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“…Therefore, not a significant loss in performance is expected by adopting such a simplified scenario. The first principal component was removed as well, since it typically addresses the overall scene brightness ( [8]). …”

Section: Design Of Experimentsmentioning

confidence: 99%

A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model

Veracini

Matteoli

Diani

et al. 2010

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing

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Anomaly Detection (AD) in remotely sensed airborne hyperspectral images has been proven valuable in many applications. Within the AD approach that defines the spectral anomalies with respect to a statistical model for the background, reliable background PDF estimation is essential to a successful outcome. This paper proposes a new Bayesian strategy for learning a non-Gaussian mixture model for the background PDF based on elliptically contoured distributions. The resulting estimated background PDF is then used to detect spectral anomalies, characterized by a low probability of occurrence with respect to the global background, through the Generalized Likelihood Ratio Test (GLRT). Real hyperspectral imagery is used for experimental evaluation of the proposed strategy

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“…The background samples support the hypersphere, and the pixels beyond the hypersphere model are considered anomalies [26]. Furthermore, the clustering-based method [27,28], such as the density-based methods [29], are adopted for hyperspectral anomaly detection, which is an important component of distance-based methods. These methods first cluster the original data, and then estimate the anomaly degree of the testing pixel [24].…”

Section: Introductionmentioning

confidence: 99%

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

Zhang

Jiao

2023

Remote Sensing

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Anomaly detection is a crucial task for hyperspectral image processing. Most popular methods detect anomalies at the pixel level, while a few algorithms for anomaly detection only utilize subpixel level unmixing technology to extract features without fundamentally analyzing the anomalies. To better detect and separate the anomalies from the background, this paper proposes a dual-view hyperspectral anomaly detection method by taking account of the anomaly analysis at both levels mentioned. At the pixel level, the spectral angular distance is adopted to calculate the similarities between the central pixel and its neighbors in order to further mine the spatial consistency for anomaly detection. On the other hand, from the aspect of the subpixel level analysis, it is considered that the difference between the anomaly and the background usually arises from dissimilar endmembers, where the unmixing will be fully implemented. Finally, the detection results of both views are fused to obtain the anomalies. Overall, the proposed algorithm not only interprets and analyzes the anomalies from dual levels, but also fully employs the unmixing for anomaly detection. Additionally, the performance of multiple data sets also confirmed the effectiveness of the proposed algorithm.

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Anomaly detection in hyperspectral imagery: comparison of methods using diurnal and seasonal data

Cited by 26 publications

References 2 publications

Greedy Ensemble Hyperspectral Anomaly Detection

Greedy Ensemble Hyperspectral Anomaly Detection

A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model

Dual-View Hyperspectral Anomaly Detection via Spatial Consistency and Spectral Unmixing

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