Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms

Manolakis, Dimitris G.; Truslow, Eric; Pieper, Michael; Cooley, T.; Brueggeman, M.

doi:10.1109/msp.2013.2278915

Cited by 232 publications

(137 citation statements)

References 28 publications

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“…The second detection strategy is a classical matched filter (Manolakis et al, 2014), a variant of which was used previously for CH 4 detection by Thorpe et al (2013). The matched filter tests each spectrum against a target signature t while accounting for the background covariance.…”

Section: Onboard Signature Detectionmentioning

confidence: 99%

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

et al. 2015

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Abstract. Localized anthropogenic sources of atmospheric CH 4 are highly uncertain and temporally variable. Airborne remote measurement is an effective method to detect and quantify these emissions. In a campaign context, the science yield can be dramatically increased by real-time retrievals that allow operators to coordinate multiple measurements of the most active areas. This can improve science outcomes for both single-and multiple-platform missions. We describe a case study of the NASA/ESA CO 2 and MEthane eXperiment (COMEX) campaign in California during June and August/September 2014. COMEX was a multi-platform campaign to measure CH 4 plumes released from anthropogenic sources including oil and gas infrastructure. We discuss principles for real-time spectral signature detection and measurement, and report performance on the NASA Next Generation Airborne Visible Infrared Spectrometer (AVIRIS-NG). AVIRIS-NG successfully detected CH 4 plumes in realtime at Gb s −1 data rates, characterizing fugitive releases in concert with other in situ and remote instruments. The teams used these real-time CH 4 detections to coordinate measurements across multiple platforms, including airborne in situ, airborne non-imaging remote sensing, and groundbased in situ instruments. To our knowledge this is the first reported use of real-time trace-gas signature detection in an airborne science campaign, and presages many future applications. Post-analysis demonstrates matched filter methods providing noise-equivalent (1σ ) detection sensitivity for 1.0 % CH4 column enhancements equal to 141 ppm m.

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Section: Onboard Signature Detectionmentioning

confidence: 99%

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

et al. 2015

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“…The observation are whitened thanks to the SCM computed on the pure noise sequence. For simplicity reason, we have directly applied the test (nonoptimal one) given by (1). First, due to the covariance whitening, one can see that the SNR of each anomaly needs to be higher for achieving similar performance.…”

Section: B Correlated Gaussian Noisementioning

confidence: 99%

“…This kind of imaging is particularly informative especially since the spectral resolution of these images is important. Thus, it enables to evaluate the kind of material or object present on an image more precisely than with only one image in one spectral band provided the materials spectral characteristics are known [1]. Since hyperspectral images contain a wide range of information, this requires adapted statistical tools.…”

Section: Introductionmentioning

confidence: 99%

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Anomaly detection and estimation in hyperspectral imaging using Random Matrix Theory tools

Terreaux

Ovarlez

Pascal

2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-Anomaly detection aims to detect sources with different spectral characteristics from the background in an hyperspectral image. Classical tools for anomaly detection and estimation are known to have poor performance when they are used on high dimensional hyperspectral image since typically both the number of available sample and their size are large for this kind of imaging. New estimation methods for the number of anomalies, adapted to large dimensional systems, are required. This article points out the limits of classical methods such as Akaike Information Criterion (AIC) or Minimum Description Length (MDL) criteria and it proposes a new estimator based on Random Matrix Theory results better adapted for hyperspectral imaging. Finally, the proposed method is validated on both Monte-Carlo simulations and on experimental data.

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“…Signal sources appear as anomalies in the data, such as unexpected presence, low probability of occurrence, small sample population whose signature is spectrally distinct from spectral signatures of its surrounding data samples. As a result, anomaly detection has received considerable interest in hyperspectral imaging in the last twenty years [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

et al. 2018

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Abstract:Anomaly detection has received considerable interest for hyperspectral data exploitation due to its high spectral resolution. Fast processing and good detection performance are practically significant in real world problems. Aiming at these requirements, this paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. This paper develops a recursive local summation RX anomaly detection approach by virtue of sliding windows. A causal sample covariance/correlation matrix is derived for local window background. As for the real-time sliding windows, the Woodbury identity is used in recursive update equations, which could avoid the calculation of historical information and thus speed up the processing. Furthermore, a background suppression algorithm is also proposed in this paper, which removes the current under test pixel from the recursively update processing. Experiments are implemented on a real hyperspectral image. The experiment results demonstrate that the proposed anomaly detector outperforms the traditional real-time local background detector and has a significant speed-up effect on calculation time compared with the traditional detectors.

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Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms

Cited by 232 publications

References 28 publications

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

Real-time remote detection and measurement for airborne imaging spectroscopy: a case study with methane

Anomaly detection and estimation in hyperspectral imaging using Random Matrix Theory tools

Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows

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