July 28 - August 2, 2019 • Yokohama, Japan

Pacifico Yokohama

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My IGARSS 2019 Schedule

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Session Detail

Session Title WEP2.PP: Big Data and Machine Learning - Machine Learning for Land Application
Presentation Mode Poster
Session Time Wednesday, 31 July, 15:20 - 16:20
Location Room 501-502: Poster Area P
Session ChairZhengwei Yang, USDA National Agricultural Statistics Service
  WEP2.PP.1: EVALUATION OF MACHINE LEARNING-BASED URBAN SURFACE MAPPING USING A NEW MODERATE-RESOLUTION SATELLITE IMAGERY DATASET
         Xin Luo; Tongji University
         Xiaohua Tong; Tongji University
         Runjie Wang; Tongji University
         Haiyan Pan; Tongji University
  WEP2.PP.2: COMPARATIVE ASSESSMENT OF MACHINE LEARNING TECHNIQUES FOR LAND USE/LAND COVER CLASSIFICATION IN THE BRAZILIAN SAVANNA USING ALOS-2/PALSAR-2 POLARIMETRIC IMAGES
         Flavio Fortes Camargo; University of Brasilia
         Edson Eyji Sano; University of Brasilia
         Jose Claudio Mura; National Institute for Space Research (INPE)
         Claudia Maria de Almeida; National Institute for Space Research (INPE)
         Tati de Almeida; University of Brasilia
  WEP2.PP.3: CLOUD DETECTION AND CLASSIFICATION FOR S-NPP FSR CRIS DATA USING SUPERVISED MACHINE LEARNING
         Miao Tian; University of Electronic Science and Technology of China
         Hao Chen; University of Electronic Science and Technology of China
         Guanghui Liu; University of Electronic Science and Technology of China
  WEP2.PP.4: CROPLAND MAPPING IN FRAGMENTED AGRICULTURAL LANDSCAPE USING MODIFIED PYRAMID SCENE PARSING NETWORK
         Junwen Yang; Beijing Normal University
         Jinshui Zhang; Beijing Normal University
         Shuang Zhu; Beijing Normal University
         Qing Xu; Beijing Normal University
         Feng Zhang; Beijing Normal University
         Zhijiang Yang; Beijing Institute of Space Launch Technology
         Zheng Dong; Beijing Vocational College of Transportation
  WEP2.PP.5: OPEN MULTI-PROCESSING ACCELERATION FOR UNSUPERVISED LAND COVER CATEGORIZATION USING PROBABILISTIC LATENT SEMANTIC ANALYSIS
         Sergio Bernabe Garcia; Complutense University of Madrid
         Carlos Garcia; Complutense University of Madrid
         Ruben Fernandez-Beltran; University Jaume I
         Mercedes E. Paoletti; University of Extremadura
         Juan M. Haut; University of Extremadura
         Javier Plaza; University of Extremadura
         Antonio Plaza; University of Extremadura
  WEP2.PP.6: DIMENSIONALITY REDUCTION IN THE PRESENCE OF HIGHLY CORRELATED VARIABLES FOR RANDOM FORESTS: WETLAND CASE STUDY
         Amir Behnamian; Environment and Climate Change Canada
         Sarah Banks; Environment and Climate Change Canada
         Lori White; Environment and Climate Change Canada
         Koreen Millard; Environment and Climate Change Canada
         Darren Pouliot; Environment and Climate Change Canada
         Jon Pasher; Environment and Climate Change Canada
         Jason Duffe; Environment and Climate Change Canada
  WEP2.PP.7: SEQUENTIAL RECURRENT ENCODERS FOR LAND COVER MAPPING IN THE BRAZILIAN AMAZON USING MODIS IMAGERY AND AUXILIARY DATASETS
         Alejandro Coca-Castro; King's College London
         Marc Rußwurm; Technical University of Munich
         Louis Reymondin; International Center for Tropical Agriculture
         Mark Mulligan; King's College London
  WEP2.PP.8: CAN A DEEP NETWORK UNDERSTAND THE LAND COVER ACROSS SENSORS?
         Zhongling Huang; Chinese Academy of Sciences
         Corneliu Octavian Dumitru; German Aerospace Center (DLR)
         Zongxu Pan; Chinese Academy of Sciences
         Bin Lei; Chinese Academy of Sciences
         Mihai Datcu; German Aerospace Center (DLR)
  WEP2.PP.9: DEEP CONVOLUTIONAL NETWORKS FOR CLOUD DETECTION USING RESOURCESAT-2 DATA
         Debvrat Varshney; Indian Institute of Remote Sensing
         Prasun Gupta; Indian Institute of Remote Sensing
         Claudio Persello; University of Twente
         Bhaskar Nikam; Indian Institute of Remote Sensing
  WEP2.PP.10: PREDICTING IMPERVIOUS LAND EXPANSION USING DEEP DECONVOLUTIONAL NEURAL NETWORKS
         Pariya Pourmohammadi; West Virginia University
         Donald Adjeroh; West Virginia University
         Michael Strager; West Virginia University