教育与工作经历

副研究员，测绘与地理信息学院，同济大学，2018.03 至今

助理研究员，测绘与地理信息学院，同济大学，2013.10-2018.03

博士后，高光谱计算实验室，埃斯特雷马杜拉大学，2014.09-2017.09（合作导师：童小华教授，Antonio Plaza）

工学博士，测绘遥感信息工程国家重点实验室，武汉大学，2008.09-2013.06(博士导师：张良培教授，钟燕飞教授)

工学学士，遥感信息工程学院，武汉大学，2004.09-2008.06

研究方向

多源遥感数据智能处理、融合与应用，三维重建与视觉导航定位研究

科研工程项目

[1]基于空地影像匹配的火星巡视器自主全局定位方法研究，国家自然科学基金专项项目，在研，项目负责人

[2]较大空谱分辨率差异下的多源遥感影像亚像元变化检测研究，国家自然科学基金面上项目，在研，项目负责人

[3]地形特征识别跟踪及增强系统，航天合作项目，在研，项目负责人

[4]新型城镇化建设与管理空间信息综合服务及应用示范，国家重点研发计划，结题，子课题负责人

[5]高光谱遥感影像混合像元分解定位联合模型研究，国家自然科学基金青年项目，结题，项目负责人

[6]基于像元混合模型的高光谱影像超分辨率重建方法研究，中国博士后科学基金(特别资助)，结题，项目负责人

[7]多时相遥感影像亚像元定位研究，中国博士后科学基金(一等资助)，结题，项目负责人

[8]分布式微纳遥感网高精度载荷数据融合与反演技术，国家重点研发计划，结题，子课题负责人

[9]基于遥感技术的城市热岛效应分析，企业合作项目，结题，项目负责人

奖励与荣誉

[1]国家教学成果二等奖，2023年

[2]中国测绘学会测绘科学技术一等奖，2023年

[3]中国测绘学会测绘科学技术一等奖，2021年

[4]中国测绘学会测绘科技进步一等奖，2019年

[5]第八届高校GIS论坛优秀教学成果奖，2020年

[6]高校GIS新锐，2020年

[7]《遥感学报》优秀审稿专家，2018年

[8]“挑战杯”全国大学生课外学术科技作品竞赛“揭榜挂帅”专项赛全国特等奖 指导教师，2023年

[9]全国大学生测绘学科科技论文竞赛特等奖 指导教师，2022年

[10]同济大学本科生学科竞赛优秀指导教师，2022年

代表性论文

[1]Xu, Xiong, Tao Cheng, Beibei Zhao, Chao Wang, Xiaohua Tong, Yongjiu Feng, Huan Xie, and Yanmin Jin. "A Novel Object Detection Method for Solid Waste Incorporating a Weighted Deformable Convolution." Photogrammetric Engineering & Remote Sensing 89, no. 11 (2023): 679-689.

[2]Xu X, Pei H, Wang C, et al. Long-term analysis of the urban heat island effect using multisource Landsat images considering inter-class differences in land surface temperature products[J]. Science of The Total Environment, 2023, 858: 159777.

[3]Wang C, Li Z, Xu X, et al. Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval[J]. Frontiers of Earth Science, 2023: 1-13.

[4]Wang, Chao, Siji Sanlang, Xiaohua Tong, Xiong Xu, Yongjiu Feng, and Zhiyuan Li. "Exploring the effects of the rocket exhaust of the Chang'E-5 lander on the lunar regolith using LROC NAC and landing camera images." Icarus (2023): 115649.

[5]曹子龙, 童小华, 许雄, 叶真, 肖长江. 基于空地影像多层级匹配的火星巡视器定位与地面验证[J]. 测绘学报, 2023, 52(4): 579-587.

[6]Liao S, Xu X, Xie H, et al. A Modified Shape Model Incorporating Continuous Accumulated Growing Degree Days for Phenology Detection of Early Rice[J]. Remote Sensing, 2022, 14(21): 5337.

[7]Xu X, Zhao B, Tong X, et al. A Data Augmentation Strategy Combining a Modified pix2pix Model and the Copy-Paste Operator for Solid Waste Detection With Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 8484-8491.

[8]Guo Y, Tong X, Xu X, et al. An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.

[9]Wang C, Li Z, Xu X, et al. A Coarse-To-Fine Approach to Detect Shadows in the Chang’E-4 VNIS Hyperspectral Images[J]. Earth and Space Science, 2022, 9(7): e2022EA002387.

[10]Pan H, Tong X, Xu X, et al. Updating of land cover maps and change analysis using globeland30 product: A case study in shanghai metropolitan area, china[J]. Remote Sensing, 2020, 12(19): 3147.

[11]Zhang H, Kang J, Xu X, et al. Accessing the temporal and spectral features in crop type mapping using multi-temporal Sentinel-2 imagery: A case study of Yi’an County, Heilongjiang province, China[J]. Computers and Electronics in Agriculture, 2020, 176: 105618.

[12]Xu X, Tong X, Plaza A, et al. A new spectral-spatial sub-pixel mapping model for remotely sensed hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6763-6778.

[13]Zhai H, Zhang H, Xu X, et al. Kernel sparse subspace clustering with a spatial max pooling operation for hyperspectral remote sensing data interpretation[J]. Remote Sensing, 2017, 9(4): 335.

[14]Xu X, Tong X, Plaza A, et al. Joint sparse sub-pixel mapping model with endmember variability for remotely sensed imagery[J]. Remote Sensing, 2016, 9(1): 15.

[15]Xu X, Tong X, Plaza A, et al. Using linear spectral unmixing for subpixel mapping of hyperspectral imagery: A quantitative assessment[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 10(4): 1589-1600.

[16]Xie H, Luo X, Xu X, et al. Automated subpixel surface water mapping from heterogeneous urban environments using Landsat 8 OLI imagery[J]. Remote sensing, 2016, 8(7): 584.

[17]Xie H, Luo X, Xu X, et al. Evaluation of Landsat 8 OLI imagery for unsupervised inland water extraction[J]. International Journal of Remote Sensing, 2016, 37(8): 1826-1844.

[18]Tong X, Xu X, Plaza A, et al. A new genetic method for subpixel mapping using hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4480-4491.

[19]Feng R, Zhong Y, Xu X, et al. Adaptive sparse subpixel mapping with a total variation model for remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2855-2872.

[20]Zhong Y, Wu Y, Xu X, et al. An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(3): 1411-1426.

[21]Xu X, Zhong Y, Zhang L. A sub-pixel mapping method based on an attraction model for multiple shifted remotely sensed images[J]. Neurocomputing, 2014, 134: 79-91.

[22]Tong X, Li X, Xu X, et al. A two-phase classification of urban vegetation using airborne LiDAR data and aerial photography[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(10): 4153-4166.

[23]Xu X, Tong X, Zhang L, et al. Unsupervised classification strategy utilizing an endmember extraction technique for airborne hyperspectral remotely sensed imagery[J]. Journal of Applied Remote Sensing, 2014, 8(1): 085090-085090.

[24]Xie H, Luo X, Xu X, et al. New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images[J]. Journal of Applied Remote Sensing, 2014, 8(1): 085098-085098.

[25]Xu X, Zhong Y, Zhang L. Adaptive subpixel mapping based on a multiagent system for remote-sensing imagery[J]. IEEE Transactions on Geoscience and Remote sensing, 2013, 52(2): 787-804.

部分发明专利

[1]一种面向火星巡视器行走能力测试的火星地面模拟场，授权号：CN112213132B

[2]一种基于空地影像匹配的火星巡视器空间定位方法及系统，申请号：CN202310916060.7

[3]一种基于加权可变形卷积目标检测方法和装置,申请号：CN202211319155.2

[4]一种用于多源Landsat影像的长时序城市热岛效应分级方法，申请号：CN202210289094.3

[5]一种遥感影像目标样本增强方法，申请号：CN202210288788.5

[6]一种基于长短期记忆神经网络的滑坡位移预测方法，申请号：CN202210289093.9

[7]一种基于区域一致性分析的Landsat影像地表温度反演优选方法，申请号：CN202110131800.7

[8]一种用于疫情的返校大数据联动管理方法，申请号：CN202010841017.5