个人信息:Personal Information
副教授 研究生导师
性别:男
毕业院校:beoplay体育提现
学历:博士研究生毕业
学位:工学博士学位
在职信息:在岗
所在单位:人工智能学院
入职时间:2019-07-26
办公地点:西电南校区网络安全创新研究大楼CII-1207
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个人简介:Personal Profile
朱浩,男,1991年生于安徽宿州,工学博士。现为beoplay体育提现人工智能学院硕士生导师,副教授。
2022年至 今 ,beoplay体育提现人工智能学院副教授;
2021年至2022年,beoplay体育提现人工智能学院讲师;
2019年至2021年进入beoplay体育提现“计算机科学与技术”学科博士后流动站;
2013年至2019年就读于beoplay体育提现电路与系统专业并获工学博士学位;
2009年至2013年就读于beoplay体育提现电子科学与技术专业并获学士学位;
主要研究方向为深度学习理论,机器学习理论,海量高分辨率遥感影像的解译等。
就相关课题在 Information Fusion、IEEE Transactions on Neural Networks and Learning Systems 、IEEE Transactions on Cybernetics、ACM International Conference on Multimedia、IEEE Transactions on Geoscience and Remote Sensing、International Journal of Applied Earth Observation and Geoinformation、Pattern Recognition、Information Science、Remote Sensing、IEEE Geoscience and Remote Sensing Letters等权威期刊和顶会上发表SCI检索论文多篇,主持和参与多项国家自然科学基金项目,担任Information Fusion、IEEE TNNLS、IEEE TYCB、IEEE TIP、IEEE TGRS、Pattern Recognition、IEEE JSTAR、Remote Sensing、IEEE Access、IEEE GRSL等业内权威期刊的审稿人。
团队招生信息
欢迎做事踏实,有韧劲、执行力强,努力肯干的学生邮件联系我,一起在科研路上探索,共同拼搏,多发高水平论文(中科院二区/CCF B类论文打底)。最好有一定的代码基础,本科专业不对口不是问题,英语差也不是问题,都小事儿。但想混日子,轻松混学位的请千万别选我,相互伤害、相互折磨图个啥!成年人嘛,要的就是一个相互坦诚,最烦说一套做一套那种耍嘴皮子的老油子人精。这里不需要八面玲珑办巧事,这里只需要有棱有角推进度。踏踏实实做东西,必然很苦,要坚持有毅力才行。但也没让你透支拼命,身体健康必然第一位,一周工作六天,该睡睡该醒醒,不会半夜呼你信息、发你邮件、敲你门窗,就是让想努力的人的汗水不白费。不虚头八脑、不被动出差,就是敲代码、搞算法、发paper、一起做做项目、提升自己、拿个好offer。科研工作,特别是第一篇,会一点点手把手,一步步教到位,本人带学生经验不少(本人三作及三作之前的论文均是直接负责、手把手带出来的),团队也有非常不错的科研氛围和阶梯传承性,有意的同学可随时邮件联系我。
PS. 鼓励有志在本科阶段做科研、出论文的同学邮件联系我。
近期研究方向:
以人工智能核心算法理论为基础,解决遥感影像多模态特征学习、多源信息融合识别、目标检测与追踪等任务。亮点工作主要包括:
建立双路径渐进融合与协同匹配网络模型,解决遥感影像特有的复杂信息不对等难题;
设计决策推理的样本增强和自步重构的误差损失策略,解决样本训练不均衡与误差构建不适应难题;
建立异源遥感影像间概率稀疏迁移学习模型,解决异源特征融合不协调与特征匹配不稳定难题。
近期课题组论文的相关部分代码整合链接如下:[Xidian-AIGroup190726]
近期发表论文(*为通讯作者,按时间序):
W. Ma, R. Jing, H. Zhu*, H. Wu, X. Yi, Y. Guo, P. Guo, Y. Wu. Dual Branch Feature Representation and Variational Autoencoder for PAN and MS Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (IF: 5.500), 2024.09.13, Accepted.
W. Ma, Y. Wu, H. Zhu*, W. Zhao, Y. Wu, B. Hou, L. Jiao. Adaptive Feature Separation Network for Remote Sensing Object Detection. IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.08.24, Accepted.
H. Zhu, W. Zhao, X. Li, B. Hou, C. Jiao, Z. Ren, W. Ma, L. Jiao. A Semantically Non-Redundant Continuous-scale Feature Network for PAN and MS Classification. IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.08.06, Accepted.
H. Zhu, F. Yan, P. Guo, X. Li, B. Hou, K. Chen, S. Wang, L. Jiao. High-low-frequency progressive guided diffusion model for PAN and MS classification, IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.06.29, Accepted.
H. Zhu, X. Yi, X. Li, B. Hou, C. Jiao, W. Ma, L. Jiao. ConvGRU-based Multi-scale Frequency Fusion Network for PAN-MS Joint Classification. IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.06.10, Accepted.
H. Zhu, P. Guo, B. Hou, X. Li, C. Jiao, B. Ren, L. Jiao, S. Wang. Few-Shot MS and PAN Joint Classification with Improved Cross-Source Contrastive Learning. IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.06.07, Accepted.
W. Ma, X. Yang, H. Zhu*, X. Wang, X. Yi, Y. Wu, B. Hou, L. Jiao. NRENet: Neighborhood Removal-and-Emphasis Network for Ship Detection in SAR Images, International Journal of Applied Earth Observation and Geoinformation (IF: 7.500), 2024.05.17, Accepted.
Z. Ren, Z. Du, S. Liu, B. Hou, W. Li, H. Zhu, B. Ren, L. Jiao. Self-supervised learning guided by SAR image factors for terrain classification. IEEE Transactions on Geoscience and Remote Sensing (IF: 8.200), 2024.04.01, Accepted.
W. Ma, X. Wang, H. Zhu*, X. Yang, X. Yi, L. Jiao. Significant Feature Elimination and Sample Assessment for Remote Sensing Small Objects Detection, IEEE Transactions on Geoscience and Remote Sensing, 2024.3.15, Accepted, (IF: 8.200, Cite: 1).
W. Ma, H. Chen, H. Zhu*, N. Li, Y. Wu, B. Hou, L. Jiao. MCDet: Multi-content Collaboration Detector for Multiscale Remote Sensing Object, IEEE Geoscience and Remote Sensing Letters, 2024, Accepted. (IF: 5.343, Cite: 0).
W. Ma, Y. Guo, H. Zhu*, X. Yi, W. Zhao, Y. Wu, B. Hou, L. Jiao. Intra- and Inter-source Interactive Representation Learning Network for Remote Sensing Images Classification, IEEE Transactions on Geoscience and Remote Sensing, 2024, Accepted, (IF: 8.200, Cite: 3).
L. Xu, H. Zhu*, L. Jiao, W. Zhao , X. Li, B. Hou, Z. Ren, W. Ma. A Dual-Stream Transformer with Diff-attention for Multisource Remote Sensing Classification, IEEE Transactions on Geoscience and Remote Sensing, 2023, Accepted. (IF: 8.200, Cite: 0)
Y. Han, H. Zhu*, L. Jiao, X. Yi, X. Li, B. Hou, W. Ma, S. Wang. SSMU-Net: A Style Separation and Mode Unification Network for Multimodal Remote Sensing Image Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, Accepted. (IF: 8.200, Cite: 2)
X. Li, L. Jiao*, H. Zhu, Z. Huang, F. Liu, L. Li, P. Chen, S. Yang. A Complex-Former Tracker with Dynamic Polar Spatio-Temporal Encoding[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. (IF: 14.225, Cite: 4)
W. Ma, M. Ma, L. Jiao*, F. Liu, H. Zhu, X. Liu, S. Yang, B Hou. An Adaptive Migration Collaborative Network for Multimodal Image Classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. (IF: 14.225, Cite: 7)
W. Ma, M. Yue, Y. Wu, Y. Yuan, H. Zhu, B. Hou, L. Jiao. Explore the Influence of Shallow Information on Point Cloud Registration[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023. (IF: 14.225, Cite: 4)
H. Zhu*, K. Sun, L. Jiao, X. Li, F. Liu, B. Hou, S. Wang. Adaptive Dual-Path Collaborative Learning for PAN and MS Classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15. (IF: 8.200, Cite: 5)
Y. Liao, H. Zhu*, L. Jiao, X. Li, N. Li, K. Sun, X. Tang, B Hou. A two-stage mutual fusion network for multispectral and panchromatic image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18. (IF: 8.200, Cite: 4)
W. Ma, N. Li, H. Zhu*, L. Jiao, X. Tang, Y. Guo, B. Hou. Feature split–merge–enhancement network for remote sensing object detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-17. (IF: 8.200, Cite: 73)
W. Ma, N. Li, H. Zhu*, K. Sun, Z. Ren, X. Tang, B. Hou, L Jiao. A collaborative correlation-matching network for multimodality remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-18. (IF: 8.200, Cite: 8)
X. Li, L. Jiao*, H. Zhu, F. Liu, S. Yang, X. Zhang, S. Wang, R Qu. A collaborative learning tracking network for remote sensing videos[J]. IEEE Transactions on Cybernetics, 2022. (IF: 19.118, Cite: 11)
W. Ma*, M. Yue, Y. Yuan, Y. Wu, H. Zhu, L. Jiao. Point Cloud Registration Based on Global and Local Feature Fusion[C]//Intelligence Science IV: 5th IFIP TC 12 International Conference, ICIS 2022, Xi'an, China, October 28–31, 2022, Proceedings. Cham: Springer International Publishing, 2022: 310-317. (CCF C, Cite: 1)
W. Ma, H. Ma, H. Zhu*, Y. Li, L. Li, L. Jiao, B Hou. Hyperspectral image classification based on spatial and spectral kernels generation network[J]. Information Sciences, 2021, 578: 435-456. (IF: 8.233, Cite: 25)
W. Ma, X. Zhou, H. Zhu*, L. Li, L. Jiao. A two-stage hybrid ant colony optimization for high-dimensional feature selection[J]. Pattern Recognition, 2021, 116: 107933. (IF: 8.518, Cite: 107)
W. Ma, J. Shen, H. Zhu*, J. Zhang, J. Zhao, B. Hou, L. Jiao. A novel adaptive hybrid fusion network for multiresolution remote sensing images classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-17. (IF: 8.200, Cite: 31)
H. Zhu, M. Ma, W. Ma, L. Jiao*, S. Hong, J. Shen, B. Hou. A spatial-channel progressive fusion ResNet for remote sensing classification[J]. Information Fusion, 2021, 70: 72-87. (IF: 18.600, Cite: 60)
A. Li, L. Jiao*, H. Zhu, L. Li, F. Liu. Multitask semantic boundary awareness network for remote sensing image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 1-14. (IF: 8.200, Cite: 77)
W. Ma, Y. Li, H. Zhu*, H. Ma, L. Jiao, J. Shen, B. Hou. A multi-scale progressive collaborative attention network for remote sensing fusion classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021. (IF: 14.225, Cite: 21)
J. Zhang, L. Jiao*, W. Ma, F. Liu, X. Liu, L. Li, H. Zhu. RDLNet: A regularized descriptor learning network[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021. (IF: 14.225, Cite: 6)
W. Ma, J. Zhao, H. Zhu*, J. Shen, L. Jiao, Y Wu, B Hou. A spatial-channel collaborative attention network for enhancement of multiresolution classification[J]. Remote Sensing, 2020, 13(1): 106. (IF: 5.349, Cite: 20)
H. Zhu*, W. Ma, L. Li, L. Jiao*, S. Yang, B. Hou. A dual–branch attention fusion deep network for multiresolution remote–sensing image classification[J]. Information Fusion, 2020, 58: 116-131. (IF: 18.600, Cite: 52)
T. Feng, L. Jiao*, H. Zhu*, L. Sun. A novel object re-track framework for 3D point clouds[C]//Proceedings of the 28th ACM International Conference on Multimedia. 2020: 3118-3126. (CCF A, Cite: 12)
G. Li, L. Li*, H. Zhu*, X. Liu, L. Jiao. Adaptive multiscale deep fusion residual network for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(11): 8506-8521. (IF: 8.200, Cite: 71)
H. Zhu, L. Jiao*, W. Ma, F. Liu, W. Zhao. A novel
neural network for remote sensing image matching[J]. IEEE transactions
on neural networks and learning systems, 2019, 30(9): 2853-2865. (IF: 14.225, Cite: 74)
W. Ma#, J. Zhang#, Y. Wu*, L. Jiao, H. Zhu, W. Zhao. A novel two-step registration method for remote sensing images based on deep and local features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4834-4843. (IF: 8.200, Cite: 126)
H. Zhu, W. Ma, B. Hou, L. Jiao*. SAR image registration based on multifeature detection and arborescence network matching[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(5): 706-710. (IF: 5.343, Cite: 49)
专利、软著及教改论文:
朱浩、易晓宇、李晓童、焦李成、侯彪。基于自步双向对抗学习的融合遥感图像分类方法。CN202210461977.8 (授权)
朱浩、马梦茹、洪世宽、马文萍、张俊、焦李成。一种基于多视点深度特征融合SENet网络的分类方法。CN202010373506.2 (授权)
朱浩、孙柯楠、焦李成、马文萍。一种多源遥感图像的逐像素分类方法、介质及设备。CN202110064881.3 (授权)
朱浩、赵文浩、李晓童、侯彪、焦昶哲、任仲乐。一种基于连续尺度特征网络的遥感多模态图像分类方法 。202410243286.X
廖怡诺、朱浩、李晓童、焦李成、侯彪。基于自适应通道的双支路互融合的图像分类方法。CN202210511831.X
李安琪、朱浩、刘俊、周聖烨、杨慧。一种基于图卷积和注意力机制的手势分割方法、系统、设备及介质。CN116993760A
马文萍、李娜、朱浩、李腾武、焦李成、侯彪、武越。一种多尺度遥感图像目标检测方法及系统。202110507602.6(授权)
马文萍、李龙伟、朱浩、武越、周晓波。基于奇异值分解和空谱域注意力机制高光谱图像分类方法。CN202010117283.3(授权)
侯彪、任仲乐、焦李成、朱浩、赵暐、刘旭、孙其功、马文萍。一种基于目标空间知识和两阶段预测学习的目标检测方法。 CN201711059887.1 (授权)
马文萍、周晓波、朱浩、武越、李龙伟。基于混合蚁群优化算法的高维特征筛选方法。CN201911041208.7
马文萍、沈建超、朱浩、武越、赵继樑。基于数据差异和多尺度特征的卷积神经网络影像分类方法。CN202010007511.1
马文萍、赵继樑、朱浩、武越、沈建超。一种残差网络多光谱图像地物分类方法。CN202010007512.6
马文萍、周晓波、朱浩、李龙伟、武越。基于相关性融合网络的多源遥感图像的逐像素分类方法。CN202010117270.6
马文萍、马昊翔、朱浩、武越、焦李成、马梦茹、李亚婷。基于空谱卷积核的高光谱图像分类方法、存储介质及设备。CN202010560570.1
马文萍、李亚婷、朱浩、武越、焦李成、马梦茹、马昊翔。一种双支路融合多尺度注意神经网络的遥感图像分类方法。CN202010561749.9
马文萍、马梦茹、朱浩、武越、焦李成。一种逐像素分类方法、存储介质及分类设备。CN202010819496.0(授权)
马文萍、陈浩、朱浩、武越、李腾武、邬一婷。基于三元特征融合的变尺度遥感图像目标检测方法。202211347000X
马梦茹、洪世宽、朱浩、李亚婷。基于视觉人脸识别签到管理系统。2019SR1248697
洪世宽、马梦茹、李亚婷、朱浩。基于深度学习的人脸识别座位空间管理系统V1.0。2019SR1267167
朱浩、赵文浩、李晓童。面向留学生的《算法设计与分析》课程的教学模式探究。教育教学论坛(教师版),2024.05.13
专著:
焦李成、侯彪、刘芳、杨淑媛、王爽、朱浩(直接执行人)、马文萍、张向荣. 遥感脑理论及应用. 清华出版社,2022.
项目课题:
国家自然科学青年科学基金项目,62006179,面向遥感影像配准的双支路协同匹配网络研究,2021-01至今,在研,主持。
某型警戒探测综合集成技术研究,中国船舶717研究所,2023.7.20-2024.7.20,主持。
***目标检测跟踪系统,***创新研究院,2024.4.1至今,主持。
中国博士后第13批特别资助(站中),2020T130492,空-谱联合深度学习下的高光谱变化检测,2020-08至今,在研,主持。
中国博士后第66批面上项目,2019M663634,基于双支路深度匹配网络的高分辨率遥感影像配准,2019-07至今,在研,主持。
2020年教师创新基金专项,XJS201904,基于双支路深度匹配网络的复杂遥感影像特征匹配,2020-01至2021-12,已结题,主持。
2022年教师创新基金专项,XJS221901,深度神经网络下的遥感多模态融合分类学习,2022-01至2023-12,在研,主持。
2022年研究生教育教学教改专项,JGYB2229,面向留学生的《算法分析与设计》课程的教学模式探究,2022-04至今,在研,主持。
2022年本科生招生培养改革专项,ZS322029,后疫情形势下的留学生招生工作现状分析与改进举措探究,2022-04至今,在研,主持。
指导竞赛:
2021年指导本科生项目“安畅护行--交通事故智慧处理系统”获第七届中国国际“互联网+”大学生创新创业大赛陕西赛区省级铜奖, 校银奖。
2022年指导本科生项目“妙手回春--基于手势识别的虚拟交互手部康复系统”获2022年中国大学生计算机设计大赛西北地区一等奖,获国家级三等奖。
2022年指导本科生项目“慧眼安行--基于深度学习的智能交通系统”获2022年中国大学生服务创新创业大赛西北地区一等奖,国家级二等奖。
主讲课程:
研究生:Algorithm Analysis and Design (硕博留学生专业课,全英文,必修,48学时)
本科生:复变函数(大二基础课,必修,32学时)、图像理解与计算机视觉(大三专业课,必修,64学时)