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Automatic fusion and classification of hyperspectral and LiDAR data using random forests.

, , , and . IGARSS, page 1245-1248. IEEE, (2014)

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Automatic fusion and classification using random forests and features extracted with deep learning., and . IGARSS, page 2943-2946. IEEE, (2015)Target Detection in Single- and Multiple-View Through-the-Wall Radar Imaging., , and . IEEE Trans. Geosci. Remote. Sens., 47 (5): 1349-1361 (2009)Parametric Waveform Design Using Discrete Prolate Spheroidal Sequences for Enhanced Detection of Extended Targets., , and . IEEE Trans. Signal Process., 60 (9): 4525-4536 (2012)Compressive sensing and adaptive direct sampling in hyperspectral imaging., , , and . Digit. Signal Process., (2014)Bootstrap-based SVM aggregation for class imbalance problems., , , , and . EUSIPCO, page 165-169. IEEE, (2015)Feature extraction in Through-the-Wall radar imaging., , , and . ICASSP, page 3562-3565. IEEE, (2010)The recursive maximum likelihood algorithm for non-stationary signals., and . ICASSP, page 3777-3780. IEEE, (2008)Segmentation by Classification for Through-the-Wall Radar Imaging Using Polarization Signatures., , and . IEEE Trans. Geosci. Remote. Sens., 50 (9): 3425-3439 (2012)Real Time Pattern Matching with Dynamic Normalization., , and . CoRR, (2019)Adaptive target detection with application to through-the- wall radar imaging., , , and . IEEE Trans. Signal Process., 58 (11): 5572-5583 (2010)