To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.
Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
Adhering to it, a model-based deep learning network is designed to restore the image.
In this paper, a new distributed mmW radar system is designed to solve this problem.
To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed.
To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work.
To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed.
The existing Video Synthetic Aperture Radar (ViSAR) moving target shadow detection methods based on deep neural networks mostly generate numerous false alarms and missing detections, because of the foreground-background indistinguishability.
First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded.
Swin Transformer serves as backbone to model long-range dependencies and generates hierarchical features maps.
Most of existing synthetic aperture radar (SAR) ship in-stance segmentation models do not achieve mask interac-tion or offer limited interaction performance.
Moving target shadows among video synthetic aperture radar (Video-SAR) images are always interfered by low scattering backgrounds and cluttered noises, causing poor detec-tion-tracking accuracy.
To address these challenges, a multi-view imputation and cross-attention network (MCNet) was proposed to integrate data imputation and MCI conversion prediction in a unified framework.
Huge imbalance of different scenes' sample numbers seriously reduces Synthetic Aperture Radar (SAR) ship detection accuracy.
1 code implementation • 8 May 2020 • Abdelrahman Abdelhamed, Mahmoud Afifi, Radu Timofte, Michael S. Brown, Yue Cao, Zhilu Zhang, WangMeng Zuo, Xiaoling Zhang, Jiye Liu, Wendong Chen, Changyuan Wen, Meng Liu, Shuailin Lv, Yunchao Zhang, Zhihong Pan, Baopu Li, Teng Xi, Yanwen Fan, Xiyu Yu, Gang Zhang, Jingtuo Liu, Junyu Han, Errui Ding, Songhyun Yu, Bumjun Park, Jechang Jeong, Shuai Liu, Ziyao Zong, Nan Nan, Chenghua Li, Zengli Yang, Long Bao, Shuangquan Wang, Dongwoon Bai, Jungwon Lee, Youngjung Kim, Kyeongha Rho, Changyeop Shin, Sungho Kim, Pengliang Tang, Yiyun Zhao, Yuqian Zhou, Yuchen Fan, Thomas Huang, Zhihao LI, Nisarg A. Shah, Wei Liu, Qiong Yan, Yuzhi Zhao, Marcin Możejko, Tomasz Latkowski, Lukasz Treszczotko, Michał Szafraniuk, Krzysztof Trojanowski, Yanhong Wu, Pablo Navarrete Michelini, Fengshuo Hu, Yunhua Lu, Sujin Kim, Wonjin Kim, Jaayeon Lee, Jang-Hwan Choi, Magauiya Zhussip, Azamat Khassenov, Jong Hyun Kim, Hwechul Cho, Priya Kansal, Sabari Nathan, Zhangyu Ye, Xiwen Lu, Yaqi Wu, Jiangxin Yang, Yanlong Cao, Siliang Tang, Yanpeng Cao, Matteo Maggioni, Ioannis Marras, Thomas Tanay, Gregory Slabaugh, Youliang Yan, Myungjoo Kang, Han-Soo Choi, Kyungmin Song, Shusong Xu, Xiaomu Lu, Tingniao Wang, Chunxia Lei, Bin Liu, Rajat Gupta, Vineet Kumar
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.