2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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SAM-9: Detection and Classification

Session Type: Poster
Time: Thursday, 10 June, 16:30 - 17:15
Location: Gather.Town
Session Chair: Vincenzo Matta, University of Salerno
 
   SAM-9.1: TARGET DETECTION FROM DISTRIBUTED PASSIVE SENSORS: SEMI-LABELED DATA QUANTIZATION
         Zachariah Sutton; University of Connecticut
         Peter Willett; University of Connecticut
         Stefano Marano; University of Salerno
 
   SAM-9.2: SPARSE FACTORIZATION-BASED DETECTION OF OFF-THE-GRID MOVING TARGETS USING FMCW RADARS
         Gilles Monnoyer de Galland; Université catholique de Louvain
         Thomas Feuillen; Université catholique de Louvain
         Luc Vandendorpe; Université catholique de Louvain
         Laurent Jacques; Université catholique de Louvain
 
   SAM-9.3: A ROBUST COPULA MODEL FOR RADAR-BASED LANDMINE DETECTION
         Afief D. Pambudi; Technsiche Universität Darmstadt
         Fauzia Ahmad; Temple University
         Abdelhak M. Zoubir; Technische Universität Darmstadt
 
   SAM-9.4: RADAR CLUTTER CLASSIFICATION USING EXPECTATION-MAXIMIZATION METHOD
         Sudan Han; Defense Innovation Institute
         Pia Addabbo; Universitá degli studi Giustino Fortunato
         Danilo Orlando; Università degli Studi "Niccoò Cusano"
         Giuseppe Ricci; Università del Salento
 
   SAM-9.5: A META-LEARNING FRAMEWORK FOR FEW-SHOT CLASSIFICATION OF REMOTE SENSING SCENE
         Pei Zhang; Northwestern Polytechnical University
         Yunpeng Bai; Northwestern Polytechnical University
         Dong Wang; Northwestern Polytechnical University
         Bendu Bai; Xi’an University of Posts and Telecommunications
         Ying Li; Northwestern Polytechnical University
 
   SAM-9.6: DIFFERENTIAL CONVOLUTION FEATURE GUIDED DEEP MULTI-SCALE MULTIPLE INSTANCE LEARNING FOR AERIAL SCENE CLASSIFICATION
         Beichen Zhou; Wuhan University
         Jingjun Yi; Wuhan University
         Qi Bi; Wuhan University