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FR4.L1: Deep Learning using Hyperspectral and SAR Imagery

Session Type: Oral
Time: Friday, July 28, 15:50 - 17:30
Location: Ballroom B
Session Chairs: Emmanuel Maggiori, Inria Sophia Antipolis-Méditerranée and Yuliya Tarabalka, Inria Sophia Antipolis-Méditerranée
 
  FR4.L1.1: TRANSFERRED DEEP LEARNING FOR HYPERSPECTRAL TARGET DETECTION
         Wei Li; Beijing University of Chemical Technology
         Guodong Wu; Beijing University of Chemical Technology
         Qian Du; Mississippi State University
 
  FR4.L1.2: FULLY CONV-DECONV NETWORK FOR UNSUPERVISED SPECTRAL-SPATIAL FEATURE EXTRACTION OF HYPERSPECTRAL IMAGERY VIA RESIDUAL LEARNING
         Lichao Mou; German Aerospace Center; Technical University of Munich
         Pedram Ghamisi; German Aerospace Center; Technical University of Munich
         Xiao Xiang Zhu; German Aerospace Center; Technical University of Munich
 
  FR4.L1.3: HYPERSPECTRAL DATA CLASSIFICATION USING CONVOLUTIONAL RECURRENT NEURAL NETWORKS
         Hao Wu; University of Hosuton
         Saurabh Prasad; University of Hosuton
 
  FR4.L1.4: UNMIXING IN THE PRESENCE OF NUISANCES WITH DEEP GENERATIVE MODELS
         Mario Parente; University of Massachusetts Amherst
         Ian Gemp; University of Massachusetts Amherst
         Ishan Durugkar; University of Massachusetts Amherst
 
  FR4.L1.5: A NOVEL CHANGE DETECTION FRAMEWORK BASED ON DEEP LEARNING FOR THE ANALYSIS OF MULTI-TEMPORAL POLARIMETRIC SAR IMAGES
         Shaunak De; Indian Institute of Technology Bombay
         Davide Pirrone; Fondazione Bruno Kessler
         Francesca Bovolo; Fondazione Bruno Kessler
         Lorenzo Bruzzone; University of Trento
         Avik Bhattacharya; Indian Institute of Technology Bombay
 
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