研究方向：人工智能与遥感数据处理包括：开放场景目标识别、神经网络记忆与推理、多模态数据耦合在农业、灾害、图像处理等领域应用。

招生专业：遥感、地信、计算机等相关专业，欢迎联系推免或报考

2016/08-2017/08,  科罗拉多大学波尔得分校，航天科学与工程系，联合培养博士

2013/09-2018/06，北京大学，地球与空间科学学院，地图学与地理信息系统 博士

2018/08-2022/09，北京师范大学，地理科学学部 遥感科学与工程研究院，讲师

2022/09-至今，北京师范大学，地理科学学部 遥感科学与工程研究院，副教授

本科生课程《时空大数据分析》

研究生课程《深度学习与遥感信息智能提取》

本科生课程《城市遥感》

本科生通识课程《时空大数据与社会感知》

国家自然科学基金委面上/青年项目，主持

北京市自然科学基金商业航天联合/青年项目，主持

国家自然科学基金委重大项目《地表异常遥感探测与即时诊断方法》，子课题，负责人

中国博士后科学基金面上/特别资助项目，主持

国家重点研发计划《多灾种综合风险防范服务产品开发与集成平台建设示范》，参与

【# 开放场景目标识别与推理】

S Wen, W Zhao*, et al. Recognizing Unknown Disaster Scenes with Knowledge Graph-based Zero-Shot Learning (KG-ZSL) Model. IEEE Transactions on Geoscience and Remote Sensing, 2024.

F Ji, W Zhao*, et al. Spectral-Spatial Evidential Learning Network for Open-Set Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2024.

【# 神经网络记忆与持续学习】

W Zhao*, R Peng, et al. Life-long learning with continual spectral-spatial feature distillation for hyperspectral image classification.  IEEE Transactions on Geoscience and Remote Sensing, 2022.

R Peng, W Zhao*, et al. Continual Contrastive Learning for Cross-Dataset Scene Classification, Remote  Sensing, 2022, 14(20), 5105.

【# 多源数据耦合与农业监测】

K Li, W Zhao*, et al. Predicting Crop Growth Patterns with Spatial–Temporal Deep Feature Exploration for Early Mapping, Remote Sensing, 2023.

K Li, W Zhao*, et al. Multi-branch self-learning Vision Transformer (MSViT) for Crop type mapping with Optical-SAR time-series. Computers and Electronics in Agriculture, 2022.

W Zhao, Y Qu, et al. Spatial-aware SAR-optical time-series deep integration for crop phenology tracking. Remote Sensing of Environment, 2022, 276, 113046.

W Zhao, Y Qu, et al. Deeply synergistic optical and SAR time series for crop dynamic monitoring. Remote Sensing of Environment, 2020, 247, 111952.  (code exe工具)

Y Qu, W Zhao*, et al. Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network, Remote Sensing, 2020, 12(15), 2493.

W Zhao, Y Bo, J Chen, et al. Exploring semantic elements for urban scene recognition: Deep integration of high-resolution imagery and OpenStreetMap (OSM). ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 151, 237-250

【# 卫星地表制图与变化检测】

W Zhao, S Peng, et al. Contextual-aware Land Cover Classification with U-shaped Object Graph Neural Network (U-OGNN). IEEE Geoscience and Remote Sensing Letters, 2022.

W Zhao, D Wu, et al. Hyperspectral image classification with multi-scale graph convolution network. International Journal of Remote Sensing, 2021, 42(21), 8380.

X Chen, W Zhao#, et al. Mapping Large-Scale Forest Disturbance Types with Multi-Temporal CNN Framework. Remote Sensing, 2021, 13(24), 5177.

W Zhao, X Chen, et al. Using adversarial network for multiple change detection in bitemporal remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 2020. (ESI)

W Zhao, X Chen, J Chen, et al. Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification. Remote Sensing, 2020, 12(5), 843. (ESI, code)

W Zhao, L Mou, J Chen, et al. Incorporating Metric Learning and Adversarial Network for Seasonal Invariant Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(4), 2720 - 2731.

W Zhao, X Chen, Y Bo, et al. Semisupervised Hyperspectral Image Classification With Cluster-Based Conditional Generative Adversarial Net. IEEE Geoscience and Remote Sensing Letters, 2019, 17(3), 539 - 543.

W Zhao, WJ. Emery, Y Bo, J Chen. Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model. Remote Sensing, 2018, 10(11).

W Zhao, S Du, Q Wang, WJ Emery. Contextually guided very-high-resolution imagery classification with semantic segments. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 132, 48-60.

W Zhao, S Du, WJ Emery. Object-based convolutional neural network for high-resolution imagery classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(7), 3386-3396. (code)

W Zhao, S Du. Spectral–spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54 (8), 4544-4554 (ESI / HOT paper, code)

W Zhao, S Du. Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113, 155-165 (ESI )

W Zhao, S Du. Scene classification using multi-scale deeply described visual words, International Journal of Remote Sensing, 2016, 37 (17), 4119-4131.

W Zhao, Z Guo, J Yue, X Zhang, L Luo. On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery,  International Journal of Remote Sensing 2015, 36 (13), 3368-3379

J Yue, W Zhao, S Mao, H Liu. Spectral–spatial classification of hyperspectral images using deep convolutional neural networks. Remote Sensing Letters 2015, 6 (6), 468-477 (ESI )

国产卫星影像全链路高效管理与智能解译处理关键技术及应用，地理信息科技进步奖，一等奖，排名4

第16届中国地理信息科学理论与方法学术年会 青年教师论文竞赛（一等奖）

2020年度北京师范大学优秀辅导员

2020年地理学部第二届青教赛一等奖

专利：

一种顾及物候特征的遥感耕地变化检测方法及系统，发明专利，ZL 2020 1 0608516.X

基于多源时序遥感深度协同下的耕地动态监测方法及系统，发明专利，ZL 2020 1 0639166.3

指导学生：

屈炀（客座），发表SCI论文5篇，核心期刊论文1篇。

陈曦（客座），发表SCI论文5篇，核心期刊论文1篇。

季风呈，研究方向：开放场景分类不确定性度量；发表SCI论文2篇

彭瑞，研究方向：持续学习与网络记忆；发表SCI论文3篇，核心期刊论文1篇

李凯源，研究方向：遥感时序智能分析；发表SCI论文2篇

温思远，研究方向：零样本推理与作物分类；发表SCI论文1篇

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