Tianwen Zhang, Ph.D., Professor of SWJTU Yanghua Fellow, is a Clarivate highly cited scholar globally and one of the top 2% of scientists in the world. He has published over 20 papers in journals such as ISPRS, TGRS, TAES, 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.

Google Scholar

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

Representative Papers

[1] T. Zhang, X. Zhang, and G. Gao, “Divergence to Concentration and Pop-ulation to Individual: A Progressive Approaching Ship Detection Paradigm for Synthetic Aperture Radar Remote Sensing Imagery,” IEEE Trans. Aerosp. Electron. Syst., pp. 1-13, early access, 2025.

[2] 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.

[3] 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 Highly Cited)

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

[5] 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 Highly Cited)

[6] 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 Hot)

[7] 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 Highly Cited)

[8] 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 Highly Cited、ESI Hot)

[9] 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 Highly Cited)

[10] 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 Highly Cited)

[11] 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 Highly Cited、ESI Hot)

[12] 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 Highly Cited、ESI Hot) (The first publicly available dataset in the field)

[13] 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 Highly Cited) (The first publicly available small target dataset in the field)

[14] 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 Highly Cited、ESI Hot)

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