no code implementations • 29 Nov 2022 • Xiaochuan Ni, Xiaoling Zhang, Xu Zhan, Zhenyu Yang, Jun Shi, Shunjun Wei, Tianjiao Zeng
To avoid missed tracking, a detection method based on deep learning is designed to thoroughly learn shadows' features, thus increasing the accurate estimation.
no code implementations • 28 Nov 2022 • Xu Zhan, Xiaoling Zhang, Mou Wang, Jun Shi, Shunjun Wei, Tianjiao Zeng
Current methods obtain undifferentiated results that suffer task-depended information retrieval loss and thus don't meet the task's specific demands well.
no code implementations • 28 Nov 2022 • Yu Ren, Xiaoling Zhang, Xu Zhan, Jun Shi, Shunjun Wei, Tianjiao Zeng
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
no code implementations • 28 Nov 2022 • Xu Zhan, Xiaoling Zhang, Wensi Zhang, Jun Shi, Shunjun Wei, Tianjiao Zeng
Adhering to it, a model-based deep learning network is designed to restore the image.
no code implementations • 21 Sep 2022 • Wensi Zhang, Xiaoling Zhang, Xu Zhan, Yuetonghui Xu, Jun Shi, Shunjun Wei
To ease this restriction, in this work an image restoration method based on the 2D spatial-variant deconvolution is proposed.
no code implementations • 21 Sep 2022 • Zhenyu Yang, Xiaoling Zhang, Xu Zhan
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.
no code implementations • 21 Sep 2022 • Xu Zhan, Xiaoling Zhang, Shunjun Wei, Jun Shi
First, to enhance the imaging quality, we propose a new imaging framework base on 2D sparse regularization, where the characteristic of scene is embedded.
no code implementations • 21 Sep 2022 • Yanqin Xu, Xiaoling Zhang, Shunjun Wei, Jun Shi, Xu Zhan, Tianwen Zhang
In this paper, a new distributed mmW radar system is designed to solve this problem.
no code implementations • 21 Sep 2022 • Yu Ren, Xiaoling Zhang, Yunqiao Hu, Xu Zhan
To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed.
no code implementations • 21 Sep 2022 • Xu Zhan, Xiaoling Zhang, Jun Shi, Shunjun Wei
To bridge this gap, in the first time, analysis and the suppression method of interferences in near-field SAR are presented in this work.
no code implementations • 7 Jul 2022 • Xiaowo Xu, Xiaoling Zhang, Tianwen Zhang, Zhenyu Yang, Jun Shi, Xu Zhan
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.