The remarkable success of adaptive cosine estimator in hyperspectral target detection

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

doi:10.1117/12.2015392

Cited by 46 publications

(30 citation statements)

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“…8 In the VNIR, where target materials usually consist of a few rare and often spectrally distinct pixels, a single pass of the PFBE procedure may be sufficient. 9 However, as we show, multiple passes over the data may improve performance when the plume is large. For simplicity, a single detector with the known chemical…”

Section: Robust Detectionmentioning

confidence: 94%

Chemical plume detection with an iterative background estimation technique

2016

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The detection of chemical vapor plumes using passive hyperspectral sensors operating in the longwave infrared is a challenging problem with many applications. For adequate performance, detection algorithms require an estimate of a scene's background statistics, including the mean and covariance. Diffuse plumes with a large spatial extent are particularly difficult to detect in single-image schemes because of contamination of background statistics by the plume. To mitigate the effects of plume contamination, a first pass of the detector can be used to create a background mask. However, large diffuse plumes are typically not removed by a single pass. Instead, contamination can be reduced by using smoothed detection results as a background mask.In the proposed procedure, a detector bank is run on the cube, and a threshold applied to produce a binary image. The binary image can be modeled as a spatial point process consisting of high density and low density regions. By applying a spatial filter to the detection image, regions with overall higher intensity are detected as containing plume and can be removed from background statistic estimates. The key intuition is that regions with a higher density of hits are more likely to contain plume since plumes are spatially contiguous. We demonstrate with real plume data that this method can drastically improve detection performance over the single-pass method, and explore tradeoffs between different filter sizes and thresholds.

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Section: Robust Detectionmentioning

confidence: 94%

Chemical plume detection with an iterative background estimation technique

2016

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“…This may appear to be a surprising oversimplification but nonetheless it is among the several accepted approaches found in the hyperspectral processing literature. 15 The final step in our process is to eliminate small random detection artifacts using morphological processing.…”

Section: S = Argmaxmentioning

confidence: 99%

SWIR active polarization imaging for material identification

et al. 2013

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Nighttime active SWIR imaging has resolution, size, weight, and power consumption advantages over passive MWIR and LWIR imagers for applications involving target identification. We propose that the target discrimination capability of active SWIR systems can be extended further by exerting polarization control over the illumination source and imager, i.e. through active polarization imaging. In this work, we construct a partial Mueller matrix imager and use laboratory derived signatures to uniquely identify target materials in outdoor scenes. This paper includes a description of the camera and laser systems as well as discussion of the reduction and analysis techniques used for material identification.

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“…14 The ACE algorithm has become particularly popular in practice because of it broad applicability, simple implementation, and speed. 15 Assume that s, x,μ ∈ R b such that s is the spectrum of a target signature, x is the spectrum of a test pixel,μ is the sample mean of the pixels, andΓ ∈ R d×d is the sample covariance of the mean subtracted pixels. The ACE detection statistic is then computed as…”

Section: Introductionmentioning

confidence: 99%

Flag-based detection of weak gas signatures in long-wave infrared hyperspectral image sequences

et al. 2016

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We present a flag manifold based method for detecting chemical plumes in long-wave infrared hyperspectral movies. The method encodes temporal and spatial information related to a hyperspectral pixel into a flag, or nested sequence of linear subspaces. The technique used to create the flags pushes information about the background clutter, ambient conditions, and potential chemical agents into the leading elements of the flags. Exploiting this temporal information allows for a detection algorithm that is sensitive to the presence of weak signals. This method is compared to existing techniques qualitatively on real data and quantitatively on synthetic data to show that the flag-based algorithm consistently performs better on data when the SINR dB is low, and beats the ACE and MF algorithms in probability of detection for low probabilities of false alarm even when the SINR dB is high.

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The remarkable success of adaptive cosine estimator in hyperspectral target detection

Cited by 46 publications

References 0 publications

Chemical plume detection with an iterative background estimation technique

Chemical plume detection with an iterative background estimation technique

SWIR active polarization imaging for material identification

Flag-based detection of weak gas signatures in long-wave infrared hyperspectral image sequences

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