Jing Ming

Jing Ming

M. Eng. at the School of Information and Communication, University of Electronic Science and Technology of China (UESTC)

Publications

Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet

Published in Remote Sensing, 2020

This work proposes a deep learning network to detect ships in the high-reolsution SAR images. The method is testified by the presented Microsoft Common Objects in Context evaluation metrics on both SAR ship detection dataset (SSDD) and TerraSAR-X high-resolution images.

Recommended citation: Wei, S.; Su, H.; Ming, J.; Wang, C.; Yan, M.; Kumar, D.; Shi, J.; Zhang, "X. Precise and Robust Ship Detection for High-Resolution SAR Imagery Based on HR-SDNet." Remote Sens. 2020, 12, 167. http://jingming2019.github.io/files/Precise-and-Robust-Ship-Detection-for-High-Resolution-SAR-Imagery-Based-on-HR-SDNet.pdf

A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D imaging

Published in IEEE Access, 2019

This work proposes a FSRARA compressed sensing algorithm to acquire high-quality LASAR 3D imaging results with high computational efficiency. Several schemes are applied to reduce the dimension of matrix operation and improve side-lobe compression ability.

Recommended citation: B. Tian, X. Zhang, S. Wei, Jing Ming, et al. "A Fast Sparse Recovery Algorithm via Resolution Approximation for LASAR 3D imaging." in IEEE Access. doi: 10.1109/ACCESS.2019.2959128 http://jingming2019.github.io/files/A-Fast-Sparse-Recovery-Algorithm-via-Resolution-Approximation-for-LASAR-3D-imaging.pdf

A CNN-based Method for SAR Image Despeckling

Published in 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019

This paper presents a modified method for SAR image despeckling based on Convolutional Neural Networks (CNNs) to remove the speckle noise of SAR images.

Recommended citation: D. J. Ma, X. L. Zhang, X. X. Tang, J. Ming, et al. "A CNN-based Method for SAR Image Despeckling." IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing SymposiumYokohama, Japan, 2019, pp. 4272-4275. doi: 10.1109/IGARSS.2019.8899122 http://jingming2019.github.io/files/A-CNN-based-Method-for-SAR-Image-Despeckling.pdf

An Autofocus Method for SAR Frequency-Domain Backprojection Imaging

Published in 2019 IEEE Radar Conference, 2019

This paper proposes a novel fast autofocus method for FDBP to compensate phase error in wavenumber domain by estimating motion error.

Recommended citation: L. M. Zhou, X. L. Zhang, J. Shi, S. J. Wei, J. Ming, et al. "An Autofocus Method for SAR Frequency-Domain Backprojection Imaging." 2019 IEEE Radar Conference (RadarConf). Boston, USA, 2019, pp. 1-5. doi: 10.1109/RADAR.2019.8835552 http://jingming2019.github.io/files/An-Autofocus-Method-for-SAR-Frequency-Domain-Backprojection-Imaging.pdf

PSF Analysis and Ground Test Results of a Novel Circular Array 3-D SAR System

Published in Journal of Radars, 2018

This paper establishes a novel Circular Array SAR system to acquire high-quality 3-D SAR imaging, and verifies its performance through Point Spread Function simulation and outdoor ground test experiment.

Recommended citation: Jing Ming, Xiaoling Zhang, Ling Pu, Jun Shi. "PSF Analysis and Ground Test Results of a Novel Circular Array 3-D SAR System." Journal of Radars. vol. 7, no. 6, pp. 770-776, Dec. 2018. doi: 10.12000/JR18068. http://jingming2019.github.io/files/PSF-Analysis-and-Ground-Test-Results-of-a-Novel-Circular-Array-3-D-SAR-System.pdf