个人信息Personal Information

学历：博士研究生毕业

学位：工学博士学位

办公地点：西南交通大学犀浦校区

在职信息：在岗

主要任职：扬华学者教授

毕业院校：电子科技大学

学科：摄影测量与遥感

所在单位：地球科学与工程学院

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个人简介Personal Profile

张天文，博士，扬华学者教授，Clarivate全球高被引学者，全球前2%顶尖科学家，在ISPRS、TGRS、TAES、JSTARS、GRSL、PR、RS等期刊发表论文20余篇，ESI高被引/热点10余篇，谷歌学术引用>4000次。

谷歌学术

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

代表性论文

[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高被引)

[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高被引)

[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热点)

[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高被引)

[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高被引、ESI热点)

[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高被引)

[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高被引)

[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高被引、ESI热点)

[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高被引、ESI热点) (领域首个公开数据集)

[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高被引) (领域首个公开小目标数据集)

[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高被引、ESI热点)

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