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A kurtosis-based test to efficiently detect targets placed in close proximity by means of local covariance-based hyperspectral anomaly detectors.

, , and . WHISPERS, page 1-4. IEEE, (2011)

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Local approach to orthogonal subspace-based target detection in hyperspectral images., , , and . WHISPERS, page 1-4. IEEE, (2009)A spectral anomaly detector in hyperspectral images based on a non-Gaussian mixture model., , , , and . WHISPERS, page 1-4. IEEE, (2010)Closed-Form Non-Parametric Admissible Detector for Solid Sub-Pixel Targets., and . IGARSS, page 2274-2277. IEEE, (2023)Improving Physical and Statistical Models for Detecting Difficult Targets with LRT Detectors in Closed-Form., , and . IGARSS, page 3959-3962. IEEE, (2020)Fluorescence LIDAR system modeling for underwater object recognition performance evaluation., , , and . IGARSS, page 4943-4946. IEEE, (2015)Target detection experiments with a non-parametric detector on a new hyperspectral data set., , and . IGARSS, page 1328-1331. IEEE, (2017)Effects of signal contamination in RX detection of local hyperspectral anomalies., , and . IGARSS, page 4845-4848. IEEE, (2012)Multi-temporal approach to atmospheric effects compensation in hyperspectral image classification., , , and . IGARSS, page 1670-1673. IEEE, (2015)Underwater Material Discriminability with Fluorescence Lidar in Unknown Environmental Conditions., and . IGARSS, page 4599-4602. IEEE, (2018)A new algorithm for local background suppression in hyperspectral target detection., , , and . WHISPERS, page 1-4. IEEE, (2010)