博士生导师
硕士生导师
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：犀浦校区四教4227
在职信息：在岗
主要任职：扬华学者教授
学科：摄影测量与遥感
所在单位：地球科学与工程学院
联系方式Other Contact Information
报考该导师研究生的方式
报考该导师研究生的方式欢迎你报考张天文老师的研究生，报考有以下方式：
1、参加江南娱乐网页版入口官网下载安装暑期夏令营活动，提交导师意向时，选择张天文老师，你的所有申请信息将发送给张天文老师，老师看到后将和你取得联系，点击此处参加夏令营活动
2、如果你能获得所在学校的推免生资格，欢迎通过推免方式申请张天文老师研究生，可以通过系统的推免生预报名系统提交申请，并选择意向导师为张天文老师，老师看到信息后将和你取得联系，点击此处推免生预报名
3、参加全国硕士研究生统一招生考试报考张天文老师招收的专业和方向，进入复试后提交导师意向时选择张天文老师。
4、如果你有兴趣攻读张天文老师博士研究生，可以通过申请考核或者统一招考等方式报考该导师博士研究生。
个人简介Personal Profile
个人简介
张天文，博士，扬华学者教授，博士生导师，Clarivate全球高被引学者，全球前2%顶尖科学家，在ISPRS、TGRS、TAES、TITS、TIM、TAP、JSTARS、GRSL、PR、RS等期刊发表论文30余篇，ESI高被引/热点10余篇，谷歌学术引用>4000次。
所属团队
虚拟地理环境团队（Leader：朱庆）=> 智能遥感处理课题组（Leader：高贵）
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. Zhanget 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. Zhangand X. Zhang, "A polarization fusion network with geometric feature embedding for SAR ship classification,"Pattern Recognit., vol. 123, p. 108365, 2022.
[5]T. Zhanget 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. Zhanget 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. Zhangand 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. Zhangand 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. Zhangand 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. Zhangand 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. Zhanget 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. Zhanget 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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