Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation

Matteoli, Stefania; Diani, Marco; Corsini, Giovanni

doi:10.1109/lgrs.2010.2090337

Cited by 26 publications

(14 citation statements)

References 15 publications

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“…To address these issues, recent work has iterated over RXD's idea, e.g., by considering subspace features [22,62], by using kernels to go beyond the Gaussian assumption [21,41], by applying dimensionality reduction [33], by improving how the background statistics are estimated [20,50], or by exploiting sparsity and compress sensing theory [23,26,72]. In this work we generalize RXD's idea by looking at it from the point of view of spectral graph theory.…”

Section: Related Workmentioning

confidence: 99%

Graph Laplacian for image anomaly detection

Verdoja

Grangetto

2020

Machine Vision and Applications

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Reed-Xiaoli Detector (RXD) is recognized as the benchmark algorithm for image anomaly detection, however it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high dimensional covariance matrix and the inability to effectively include spatial awareness in its evaluation. In this work a novel graph-based solution to the image anomaly detection problem is proposed; leveraging the Graph Fourier Transform, we are able to overcome some of RXD's limitations while reducing computational cost at the same time. Tests over both hyperspectral and medical images, using both synthetic and real anomalies, prove the proposed technique is able to obtain significant gains over performance by other algorithms in the state-of-the-art.

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Section: Related Workmentioning

confidence: 99%

Graph Laplacian for image anomaly detection

Verdoja

Grangetto

2020

Machine Vision and Applications

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“…Indeed, it is widely accepted that the background is composed of a few number of representative materials, known as endmembers. In fact, many AD schemes exploit this situation, such as the Robust Principal Components Analysis (RPCA)AD [9] or the Low-Rank and Sparse Matrix Decomposition (LRaSMD) AD [25,26] and their improvements [27,28] . Hence, under such a widespread hypothesis, we will show in Section 2 how to get a relevant estimation of β.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection for replacement model in hyperspectral imaging

2021

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In this paper we consider Anomaly Detection in the hyperspectral context, and we extend the popular RX detector, initially designed under the standard additive model, to the replacement model case. Indeed, in this more realistic framework, the target, if present, is supposed to replace a part of the background. We show how to estimate this background power variation to improve the standard RX scheme. The obtained Replacement RX (RRX) is shown to be closed-form and outperforms the standard RX on a real data benchmark experiment.

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“…For probabilistic models, this contamination results in a corruption of the estimated background covariance matrix [27]. Therefore, many robust covariance matrix estimation methods, such as quasi-local estimation [28] and minimum covariance determinant [29], have been proposed. For subspace models, this contamination results in a corruption of the estimated background subspace.…”

Section: Introductionmentioning

confidence: 99%

A Constrained Sparse-Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Imagery

Ling

Guo

Lin

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a novel constrained sparserepresentation-based binary hypothesis model for target detection in hyperspectral imagery. This model is based on the concept that a target pixel can only be linearly represented by the union dictionary combined by the background dictionary and target dictionary, while a background pixel can be linearly represented by both the background dictionary and the union dictionary. To be physically meaningful, the non-negativity constraint is imposed to the weight vector. To suppress the target signals in the background dictionary, the upper bound constraint is also imposed to the weight vector. These upper bounds are adaptively estimated by the similarities between the atoms in the background dictionary and target. Then, the weight vectors under different hypotheses are recovered by a fast coordinate descent method. Finally, both the residual difference and weight difference between the two hypotheses are used to perform the target detection. An important advantage of the proposed method is the robustness to varying target contamination. Extensive experiments conducted on real and synthetic hyperspectral datasets have demonstrated the superiority of the proposed detector in detection performance and computational cost. Specifically, for the Avon dataset, our method achieves the highest area under the receiver operating characteristic (ROC) curve of 99.91%, and achieves the shortest runtime of 109.76 s.

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Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation

Cited by 26 publications

References 15 publications

Graph Laplacian for image anomaly detection

Graph Laplacian for image anomaly detection

Anomaly detection for replacement model in hyperspectral imaging

A Constrained Sparse-Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Imagery

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