Personal Profile

Tianwen Zhang, Ph.D., Professor of SWJTU Yanghua Fellow, Ph.D. supervisor, is a Clarivate highly cited scholar globally and one of the top 2% of scientists in the world. He has published over 30 papers in journals such as ISPRS, TGRS, TAES, TITS, TIM, TAP, JSTARS, GRSL, PR, RS, and more. He has also been cited over 10 times in ESI as a highly cited/hot topic, and has been cited more than 4000 times on Google Scholar.

Team Affiliation

Virtual Geographical Environment Team (Leader: Qing Zhu) => Intelligent Remote Sensing Processing Group (Leader: Gui Gao)

Google Scholar

https://scholar.google.com/citations?user=aJV0kM4AAAAJ&hl

Representative Papers

[1]  T. Zhang, G. Gao, and X. Zhang, “Glance-Focus-Gaze: A Novel Eagle-Eye Vision-Inspired Panorama-Population-Individual Progressive Screening Paradigm to Capture Ships in SAR Images,” ISPRS J. Photogramm. Remote Sens., early access, 2026.

[2] T. Zhang, X. Zhang, and G. Gao, “Density Knowledge Mining for Quantity-Aware Marine Vessel Surveillance Using Satellite SAR Data,” IEEE Trans. Ind. Inf., early access, 2026.

[3] T. Zhang and X. Zhang, “Triple-Level Sparsity Awareness for Marine Ship Surveillance Using Satellite Synthetic Aperture Radar,” IEEE Trans. Autom. Sci. Eng., vol. 23, pp. 5155-5166, 2026.

[4] T. Zhang, X. Zhang, and G. Gao, “Divergence to Concentration and Population to Individual: A Progressive Approaching Ship Detection Paradigm for Synthetic Aperture Radar Remote Sensing Imagery,” IEEE Trans. Aerosp. Electron. Syst., vol. 62, pp. 1325-1338, 2026.

[5] T. Zhang, G. Gao, X. Ke, and X. Zhang, “Swarm Learning: Perception-Retrieval-Localization for Ship Detection from Synthetic Aperture Radar Remote Sensing Imagery,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., pp. 1-11, 2026.

[6] T. Zeng, T. Zhang (共一), et al., "CFAR-DP-FW: A CFAR-Guided Dual-Polarization Fusion Framework for Large-Scene SAR Ship Detection," IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 17, pp. 7242-7259, 2024.

[7] T. Zhang et al., "HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification," IEEE Trans. Geosci. Remote Sens., vol. 60, pp. 1-22, 2022. (ESI高被引)

[8] T. Zhang and X. Zhang, "A polarization fusion network with geometric feature embedding for SAR ship classification," Pattern Recognit., vol. 123, p. 108365, 2022.

[9] T. Zhang et al., "Balance learning for ship detection from synthetic aperture radar remote sensing imagery," ISPRS J. Photogramm. Remote Sens., vol. 182, pp. 190-207, 2021. (ESI高被引)

[10] T. Zhang, X. Zhang, J. Shi, and S. Wei, "HyperLi-Net: A hyper-light deep learning network for high-accurate and high-speed ship detection from synthetic aperture radar imagery," ISPRS J. Photogramm. Remote Sens., vol. 167, pp. 123-153, 2020. (ESI热点)

[11] T. Zhang et al., "Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Images," IEEE Geosci. Remote Sens. Lett., vol. 19, 2022, Art. no. 4004905. (ESI高被引)

[12] T. Zhang and X. Zhang, "Squeeze-and-Excitation Laplacian Pyramid Network With Dual-Polarization Feature Fusion for Ship Classification in SAR Images," IEEE Geosci. Remote Sens. Lett., vol. 19, pp. 1-5, 2022. (ESI高被引、ESI热点)

[13] T. Zhang and X. Zhang, "A Full-Level Context Squeeze-and-Excitation ROI Extractor for SAR Ship Instance Segmentation," IEEE Geosci. Remote Sens. Lett., vol. 19, 2022, Art. no. 4506705. (ESI高被引)

[14] T. Zhang and X. Zhang, "A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentation," IEEE Geosci. Remote Sens. Lett., vol. 19, 2022, Art. no. 4511005. (ESI高被引)

[15] T. Zhang and X. Zhang, "ShipDeNet-20: An Only 20 Convolution Layers and <1-MB Lightweight SAR Ship Detector," IEEE Geosci. Remote Sens. Lett., vol. 18, no. 7, pp. 1234-1238, 2021. (ESI高被引、ESI热点)

[16] T. Zhang et al., "SAR Ship Detection Dataset (SSDD): Official Release and Comprehensive Data Analysis," Remote Sens., vol. 13, no. 18, pp. 1–41, 2021, Art. no. 3690. (ESI高被引、ESI热点) (领域首个公开数据集)

[17] T. Zhang et al., "LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images," Remote Sens., vol. 12, no. 18, 2020, Art. no. 2997. (ESI高被引) (领域首个公开小目标数据集)

[18] T. Zhang, X. Zhang, and X. Ke, "Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection," Remote Sens., vol. 13, no. 14, 2021, Art. no. 2771. (ESI高被引、ESI热点)

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