FR3.R1.5

NOISE-TOLERANT UNSUPERVISED CLASSIFICATION FOR POLSAR IMAGES VIA DEEP CLUSTERING AND MARKOV RANDOM FIELD

Sihan Yang, Haixia Bi, Xiaotian Wang, Xi'an Jiaotong University, China; Danfeng Hong, Chinese Academy of Sciences, China
Session:
FR3.R1: Semantic Segmentation of PolSAR Images Oral
Track:
Data Analysis
Location:
Room B
Presentation Time:
Fri, 21 Jul, 13:48 - 14:00 Pacific Time (UTC -7)
Session Co-Chairs:
Gabriele Moser, University of Genoa and Ronny Hänsch, German Aerospace Center
Session Manager:
Sumesh K C 0
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Session FR3.R1
FR3.R1.1: CONTEXTUAL CLASSIFICATION OF POLARIMETRIC SAR DATA THROUGH A COMPLEX-VALUED KERNEL AND GLOBAL ENERGY MINIMIZATION
Ignacio Masari, Gabriele Moser, Sebastiano Serpico, University of Genoa, Italy
FR3.R1.2: COMPLEX-VALUED SELF-SUPERVISED POLSAR IMAGE CLASSIFICATION INTEGRATING ATTENTION MECHANISM
Zuzheng Kuang, Haixia Bi, Fan Li, Xi'an Jiaotong University, China
FR3.R1.3: COMPLEX-VALUED FULLY CONVOLUTIONAL NETWORK FOR PolSAR IMAGE CLASSIFICATION WITH NOISY LABELS
Ningwei Wang, Xi’an Jiaotong University, China; Haixia Bi, Xiaotian Wang, Xi'an Jiaotong University, China; Zhao Chen, Donghua University, China
FR3.R1.4: Wishart-UMAP - Translating Pixel Similarity of PolSAR Images to Real-valued Feature Descriptors
Sylvia Hochstuhl, Karlsruhe Institute of Technology, Germany; Horst Hammer, Fraunhofer IOSB, Germany; Antje Thiele, Stefan Hinz, Karlsruhe Institute of Technology, Germany
FR3.R1.5: NOISE-TOLERANT UNSUPERVISED CLASSIFICATION FOR POLSAR IMAGES VIA DEEP CLUSTERING AND MARKOV RANDOM FIELD
Sihan Yang, Haixia Bi, Xiaotian Wang, Xi'an Jiaotong University, China; Danfeng Hong, Chinese Academy of Sciences, China
Resources
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