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Automated Underwater Object Recognition by Means of Fluorescence LIDAR.

, , , , and . IEEE Trans. Geosci. Remote. Sens., 53 (1): 375-393 (2015)

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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)Recognizing Submerged Materials with Fluorescence Lidar without Knowledge of Environmental Conditions., , and . IGARSS, page 82-85. IEEE, (2019)Nonparametric Target Detection with Target Strength Estimation for Hyperspectral Images., , and . IGARSS, page 449-452. IEEE, (2019)Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation., , and . IEEE Geosci. Remote. Sens. Lett., 8 (3): 532-536 (2011)A novel technique for hyperspectral signal subspace estimation in target detection applications., , , , and . IGARSS (3), page 95-98. IEEE, (2008)A framework for predicting underwater object recognition performance with fluorescence LIDAR., , , and . IGARSS, page 2302-2305. IEEE, (2017)