WE1.R5.5

Classification of Martian terrains via deep clustering of Mastcam images

Mario Parente, Tejas Panambur, University of Massachussets Amherst, United States

Session:
Advanced Clustering Methods for Remote Sensing Data I

Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)

Presentation Time:
Wed, 30 Sep, 12:40-12:50 (UTC)
Wed, 30 Sep, 20:40-20:50 China Standard Time (UTC +8)
Wed, 30 Sep, 14:40-14:50 Central Europe Summer Time (UTC +2)
Wed, 30 Sep, 05:40-05:50 Pacific Daylight Time (UTC -7)

Session Co-Chairs:
Qian Du, Mississippi State University and Mario Parente, University of Massachussets Amherst
Session Managers:
Iman Heidarpour Shahrezaei and Mohammad Al-Khaldi

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Session

WE1.R5.1: L0-Motivated Low Rank Sparse Subspace Clustering for Hyperspectral Imagery
Long Tian, Qian Du, Mississippi State University, United States; Ivica Kopriva, Ruđer Bošković Institute, Croatia (Hrvatska)
WE1.R5.2: PATCH-BASED DIFFUSION LEARNING FOR HYPERSPECTRAL IMAGE CLUSTERING
James Murphy, Tufts University, United States
WE1.R5.3: LOCALLY CONSTRAINED COLLABORATIVE REPRESENTATION BASED FISHER’S LDA FOR CLUSTERING OF HYPERSPECTRAL IMAGES
Siyu Liu, Nan Huang, Liang Xiao, Nanjing University of Science and Technology, China
WE1.R5.4: SATELLITE AGRICULTURAL MONITORING IN UKRAINE AT COUNTRY LEVEL: WORLD BANK PROJECT
Nataliia Kussul, Andrii Shelestov, Space Research Institute National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Ukraine; Hanna Yailymova, Earth Observing System Data Analytics, Ukraine; Bohdan Yailymov, Mykola Lavreniuk, Space Research Institute National Academy of Sciences of Ukraine and State Space Agency of Ukraine, Ukraine; Matviy Ilyashenko, Earth Observing System Data Analytics, Ukraine
WE1.R5.5: Classification of Martian terrains via deep clustering of Mastcam images
Mario Parente, Tejas Panambur, University of Massachussets Amherst, United States
WE1.R5.6: SCALING UP A MULTISPECTRAL RESNET-50 TO 128 GPUS
Rocco Sedona, Gabriele Cavallaro, Jenia Jitsev, Alexandre Strube, Morris Riedel, Forschungszentrum Jülich, Germany; Matthias Book, University of Iceland, Iceland
WE1.R5.7: SPATIAL-SPECTRAL SMOOTH GRAPH CONVOLUTIONAL NETWORK FOR MULTISPECTRAL POINT CLOUD CLASSIFICATION
Qingwang Wang, Harbin Institute of Technology, China; Xiangrong Zhang, Heilongjiang Institute Technology, China; Yanfeng Gu, Harbin Institute of Technology, China
WE1.R5.8: INFLUENCE OF ALEATORIC UNCERTAINTY ON SEMANTIC CLASSIFICATION OF AIRBORNE LIDAR POINT CLOUDS: A CASE STUDY WITH RANDOM FOREST CLASSIFIER USING MULTISCALE FEATURES
Jaya Sreevalsan-Nair, Pragyan Mohapatra, International Institute of Information Technology, Bangalore, India
WE1.R5.9: GLOBAL SEMANTIC LAND USE/LAND COVER BASED ON HIGH RESOLUTION SATELLITE IMAGERY USING ENSEMBLE NETWORKS
Gustav Tapper, Carl Sundelius, Leif Haglund, Vricon, Sweden
WE1.R5.10: UNSUPERVISED DOMAIN ADAPTATION TECHNIQUES FOR CLASSIFICATION OF SATELLITE IMAGE TIME SERIES
Benjamin Lucas, Monash University, Australia; Charlotte Pelletier, Bretagne-Sud University, France; Daniel Schmidt, Geoffrey Webb, Francois Petitjean, Monash University, Australia
WE1.R5.11: APPLYING A PHENOLOGICAL OBJECT-BASED IMAGE ANALYSIS (PHENOBIA) FOR AGRICULTURAL LAND CLASSIFICATION: A STUDY CASE IN THE BRAZILIAN CERRADO
Hugo Bendini, Leila Fonseca, Anderson Soares, INPE, Brazil; Philippe Rufin, Marcel Schwieder, Humboldt-Universität zu Berlin, Germany; Marcos Rodrigues, Raian Maretto, Thales Korting, INPE, Brazil; Pedro Leitao, Humboldt-Universität zu Berlin, Portugal; Ieda Sanches, INPE, Brazil; Patrick Hostert, Humboldt-Universität zu Berlin, Germany