17 - 22 July, 2022 • Physical: Kuala Lumpur Convention Centre (KLCC), Kuala Lumpur, Malaysia • Hybrid: Online Platform

Preserving Our Heritage, Enabling Our Future Through Remote Sensing

17 - 22 July, 2022 • Physical: Kuala Lumpur Convention Centre (KLCC), Kuala Lumpur, Malaysia • Hybrid: Online Platform

MO5.V5.2

Multi Scale Ship Detection Based on Attention and Weighted Fusion Model for High Resolution SAR Images

Lamei Zhang, Zhongye Chu, Bin Zou, Harbin Institute of Technology, China
Session:
Object Detection and Recognition II Oral Virtual
Track:
Data Analysis Methods (Optical, Multispectral,Hyperspectral, SAR)
Location:
Virtual Room 5
Presentation Time:
Mon, 18 Jul, 20:05 - 20:20 Malaysia Time (UTC +8)
Mon, 18 Jul, 14:05 - 14:20 Central European Time (UTC +1)
Mon, 18 Jul, 12:05 - 12:20 UTC
Mon, 18 Jul, 08:05 - 08:20 Eastern Time (UTC -5)
Session Co-Chairs:
Mario Costantini, e-GEOS – an ASI / Telespazio Company and Lamei Zhang, Harbin Institute of Technology
Session Manager:
Chiong Hieng Tiong
Presentation
Discussion
Resources
No resources available.
Session MO5.V5
MO5.V5.1: REINFORCEMENT-BASED FRUGAL LEARNING FOR INTERACTIVE SATELLITE IMAGE CHANGE DETECTION
Sebastien Deschamps, Sorbonne University and Thales, France; Hichem Sahbi, CNRS Sorbonne University, France
MO5.V5.2: Multi Scale Ship Detection Based on Attention and Weighted Fusion Model for High Resolution SAR Images
Lamei Zhang, Zhongye Chu, Bin Zou, Harbin Institute of Technology, China
MO5.V5.3: FASTER SHIP DETECTION ALGORITHM IN LARGE-SCENE SAR IMAGES
Changgui Xu, Bo Zhang, Ji Ge, Hong Zhang, Chao Wang, Xin Zhao, Liutong Li, International Research Center of Big Data for Sustainable Development Goals, Aerospace Information Research Institute, Chinese Academy of Sciences;University of Chinese Academy of Sciences, China; Fan Wu, International Research Center of Big Data for Sustainable Development Goals, Aerospace Information Research Institute, Chinese Academy of Sciences, China
MO5.V5.4: SAR IMAGE DATA AUGMENTATION VIA RESIDUAL AND ATTENTION-BASED GENERATIVE ADVERSARIAL NETWORK FOR SHIP DETECTION
Yu-Shi Guo, Heng-Chao Li, Wen-Shuai Hu, Wei-Ye Wang, Southwest Jiaotong University, China
MO5.V5.5: A DEEP LEARNING APPROACH TO SHIP DETECTION AND CHARACTERIZATION FROM MULTIRESOLUTION SATELLITE SAR IMAGES
Sergio Povoli, Lorenzo Bruzzone, University of Trento, Italy; Mauro Di Donna, Flavia Macina, Corrado Avolio, Massimo Zavagli, Mario Costantini, e-GEOS – an ASI / Telespazio Company, Italy
MO5.V5.6: FEATURE-TRANSFERABLE PYRAMID NETWORK FOR DENSE MULTI-SCALE OBJECT DETECTION IN SAR IMAGES
Zheng Zhou, Zongyong Cui, Zongjie Cao, Jianyu Yang, University of Electronic Science and Technology of China, China