IEEE IGARSS 2022 || Kuala Lumpur, Malaysia || 17

WE2.MMA: Object Recognition and Classification

Wed, 20 Jul, 11:00 - 12:00 Malaysia Time (UTC +8)
Wed, 20 Jul, 05:00 - 06:00 Central European Time (UTC +2)
Wed, 20 Jul, 03:00 - 04:00 UTC
Tue, 19 Jul, 23:00 - 00:00 Eastern Time (UTC -4)

Location: Grand Ballroom 1/2

Session Type: Multimedia In-Person

Session Co-Chairs: Xiao Xiang Zhu, Technical University of Munich, German Aerospace Center and Yuding Wang, Uinversity of Rennes 1

Session Manager: Farrah Nurdiana Ibrahim

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

WE2.MMA.1: Distributions of Subsurface Anomaly Patterns using Machine Learning Tools

Dr John Stephen Kayode, Nigerian Army University, Nigeria; Mohd Nawawi Mohd Nordin, Yusri Yusup, Universiti Sains Malaysia, Malaysia; Amin E. Khalil, Helwan University, Egypt; Mohd Hariri Arifin, Universiti Kebangsaan Malaysia, Malaysia

WE2.MMA.2: COMBINE HISTOGRAM MATCHING AND DOMAIN ADAPTATION TO COPE WITH TEMPORAL TRANSFER LEARNING FOR THE SEMANTIC SEGMENTATION OF VHR IMAGES

Dino Ienco, Kenji Ose, INRAE, France

WE2.MMA.3: DATA STREAM UNSUPERVISED PARTITIONING METHOD

Yuding Wang, Kacem Chehdi, Claude Cariou, Benoit Vozel, Uinversity of Rennes 1, France

WE2.MMA.4: DEEP LEARNING ROBUSTNESS TO DOMAIN SHIFTS DURING SEASONAL VARIATIONS

Georgios Voulgaris, Andrew Philippides, Novi Quadrianto, University of Sussex, United Kingdom

WE2.MMA.5: COMPLEX-VALUED VS. REAL-VALUED CONVOLUTIONAL NEURAL NETWORK FOR POLSAR DATA CLASSIFICATION

Reza Mohammadi Asiyabi, Andrei Anghel, CEOSpaceTech Research Center, University POLITEHNICA of Bucharest, Romania; Mihai Datcu, Earth Observation Center (EOC), German Aerospace Center (DLR), Germany; Holger Nies, Center for Sensor Systems (ZESS), University of Siegen, Germany

WE2.MMA.1: Distributions of Subsurface Anomaly Patterns using Machine Learning Tools

WE2.MMA.2: COMBINE HISTOGRAM MATCHING AND DOMAIN ADAPTATION TO COPE WITH TEMPORAL TRANSFER LEARNING FOR THE SEMANTIC SEGMENTATION OF VHR IMAGES

WE2.MMA.3: DATA STREAM UNSUPERVISED PARTITIONING METHOD

WE2.MMA.4: DEEP LEARNING ROBUSTNESS TO DOMAIN SHIFTS DURING SEASONAL VARIATIONS

WE2.MMA.5: COMPLEX-VALUED VS. REAL-VALUED CONVOLUTIONAL NEURAL NETWORK FOR POLSAR DATA CLASSIFICATION

WE2.MMA.6: AN APPROCH TO DETECT LOW AND HIGH DIELECTRIC TARGETS BEHIND THE WALL WITH THROUGH-WALL IMAGING SYSTEM

WE2.MMA.7: HYPERSPECTRAL SALIENT OBJECT DETECTION USING EXTENDED MORPHOLOGY WITH CNN

WE2.MMA.8: AZIMUTH SHIFT COMPENSATION ON SAR IMAGE-BASED VESSEL DETECTION

WE2.MMA.9: STONE PINE (Pinus Pinea L.) DETECTION FROM HIGH-RESOLUTION UAV IMAGERY USING DEEP LEARNING MODEL