Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter

Kåsen, Ingebjørg; Goa, Pål Erik; Skauli, T.

doi:10.1117/12.578782

Cited by 17 publications

(17 citation statements)

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“…Future investigations include the study of unmixing algorithms for the joint estimation of endmembers and abundances. It would also be interesting to generalize the proposed approach to more sophisticated noise models described by statistical mixtures [9] or sums of several multivariate normal probability distributions [10].…”

Section: Discussionmentioning

confidence: 99%

Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

Dobigeon

Tourneret

IlI

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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This paper addresses the problem of unmixing hyperspectral images contamined by additive colored noise. Each pixel of the image is modeled as a linear combination of pure materials (denoted as endmembers) corrupted by an additive zero mean Gaussian noise sequence with unknown covariance matrix. Appropriate priors are defined ensuring positivity and additivity constraints on the mixture coefficients (denoted as abundances). These coefficients as well as the noise covariance matrix are then estimated from their joint posterior distribution. A Gibbs sampling strategy generates abundances and noise covariance matrices distributed according to the joint posterior. These samples are then averaged for minimum mean square error estimation.Index Terms-Bayesian inference, Monte Carlo methods, spectral unmixing, hyperspectral images.

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Section: Discussionmentioning

confidence: 99%

Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

Dobigeon

Tourneret

IlI

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…The anomaly detection algorithm used here is based on a global multivariate normal mixture model representation of the background clutter, as discussed in [5]. The basic steps in this processing are:…”

Section: Anomaly Detection Algorithmmentioning

confidence: 99%

“…In contrast, mixture models, such as the multivariate normal mixture model, may be able to represent the background variability quite accurately, resulting in statistically meaningful background metrics. The characteristics of anomaly detection based on normal mixture models are discussed in some detail in [5]. This anomaly detector has demonstrated good detection performance on several occasions.…”

Section: Introductionmentioning

confidence: 99%

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Tarabalka

Haavardsholm

Kåsen

et al. 2008

J Real-Time Image Proc

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Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multicore central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable realtime anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.

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“…Mixture Models (MMM) [13]. A Stochastic Expectation Maximization (SEM) algorithm [23] is used for fitting a multivariate normal mixture model to the image for describing the background.…”

Section: Methods Based On Multivariate Normalmentioning

confidence: 99%

“…In complex scenes the latter category was shown to be very effective and several segmentation-based anomaly detectors (SBAD), not necessarily based on RX, have recently been proposed [13][14][15][16][17][18][19][20]. The aim of the current paper is to compare the results obtained by different types of anomaly detectors in scenes characterized by different types of background.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity

Borghys

Kåsen

Achard

et al. 2012

Journal of Electrical and Computer Engineering

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Anomaly detection (AD) in hyperspectral data has received a lot of attention for various applications. The aim of anomaly detection is to detect pixels in the hyperspectral data cube whose spectra differ significantly from the background spectra. Many anomaly detectors have been proposed in the literature. They differ in the way the background is characterized and in the method used for determining the difference between the current pixel and the background. The most well-known anomaly detector is the RX detector that calculates the Mahalanobis distance between the pixel under test (PUT) and the background. Global RX characterizes the background of the complete scene by a single multivariate normal probability density function. In many cases, this model is not appropriate for describing the background. For that reason a variety of other anomaly detection methods have been developed. This paper examines three classes of anomaly detectors: subspace methods, local methods, and segmentation-based methods. Representative examples of each class are chosen and applied on a set of hyperspectral data with diverse complexity. The results are evaluated and compared.

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Target detection in hyperspectral images based on multicomponent statistical models for representation of background clutter

Cited by 17 publications

References 0 publications

Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

Bayesian linear unmixing of hyperspectral images corrupted by colored Gaussian noise with unknown covariance matrix

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Hyperspectral Anomaly Detection: Comparative Evaluation in Scenes with Diverse Complexity

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