西安电子科技大学人工智能学院导师教师师资介绍简介-曹向海

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基本信息
曹向海　副教授硕士生导师
硕士学科：电路与系统　
工作单位：人工智能学院智能感知与图像理解教育部重点实验室

联系方式
通信地址：西安电子科技大学224信箱
电子邮箱：caoxh@xidian.edu.cn
办公地点：主楼III区402

个人简介
2008年3月于西安电子科技大学信号与信息处理专业获得博士学位，西安电子科技大学智能感知与图像理解教育部重点实验室成员,IEEE会员。2014.6-2015.6在澳大利亚悉尼科技大学从事访问研究。已发表SCI检索论文20多篇，长期为IEEE TNNS, IEEE TGRS, IEEE JSTARS, IEEE GRSL, International Journal of Remote Sensing, Remote Sensing Letters 等期刊审稿。担任European Journal of Remote Sensing (IF=2.808)期刊编辑。

主要研究方向
在校前期主要以工程研发为主，设计并实现了高速大容量器、通信信号检测系统以及车载雷达等多种设备；目前则以理论研究为主，研究方向为遥感图像处理、深度学习等。已以第一作者发表SCI论文20多篇，申请发明专利8项，授权6项。
已发表或录用论文：
2021
1. Cao X, Z Liu, X Li, Q Xiao, J Feng and L Jiao.Non-overlapped Sampling for Hyperspectral Imagery: Performance Evaluation and A Co-training Based Classification Strategy. IEEE Transactions on Geoscience and Remote Sensing. Accepted.
2. Feng, J., Li, D., Gu, J., Cao, X., Shang, R., Zhang, X., & Jiao, L. (2021). Deep Reinforcement Learning for Semisupervised Hyperspectral Band Selection.IEEE Transactions on Geoscience and Remote Sensing.
2020
1.Xianghai Cao,Da Wang,Xiaozhen Wang,Jing Zhao&Licheng Jiao, Hyperspectral imagery classification with cascaded support vector machines and multi-scale superpixel segmentation,International Journal of Remote Sensing, 2020, 41(12):4528-4548.
2. Zhao, J., Ba, Z., Cao, X., Feng, J., & Jiao, L. (2020). Deep Mutual-Teaching for Hyperspectral Imagery Classification.IEEE Geoscience and Remote Sensing Letters.
3. Feng, J., Chen, J., Sun, Q., Shang, R., Cao, X., Zhang, X., & Jiao, L. (2020). Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection.IEEE Transactions on Cybernetics.
4. Cao, X., Ren, M., Zhao, J., Lu, H., & Jiao, L. (2020). Non-overlapping classification of hyperspectral imagery based on set-to-sets distance.Neurocomputing,378, 422-434.
5. Feng, J., Feng, X., Chen, J., Cao, X., Zhang, X., Jiao, L., & Yu, T. (2020). Generative adversarial networks based on collaborative learning and attention mechanism for hyperspectral image classification.Remote Sensing,12(7), 1149.
2019
1. Cao X, Lu H, Ren M, et al. Non-overlapping classification of hyperspectral imagery with superpixel segmentation[J]. Applied Soft Computing, 2019, 83: 105630.
2. Cao, X., Wen, L., Ge, Y., Zhao, J., & Jiao, L. (2019). Rotation-Based Deep Forest for Hyperspectral Imagery Classification.IEEE Geoscience and Remote Sensing Letters. 16(7): 1105-1109.
3. Cao, X., Ge, Y., Li, R., Zhao, J., & Jiao, L. (2019). Hyperspectral imagery classification with deep metric learning.Neurocomputing,356, 217-227.
4. Cao X, Wei C, Ge Y, et al. Semi-Supervised Hyperspectral Band Selection Based on Dynamic Classifier Selection[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(4): 1289-1298.
5.Cao, X., Li, R., Ge, Y., Wu, B., & Jiao, L. (2019). Densely connected deep random forest for hyperspectral imagery classification.International Journal of Remote Sensing,40(9), 3606-3622.
6.Feng, J., Yu, H., Wang, L., Cao, X., Zhang, X., & Jiao, L. (2019). Classification of Hyperspectral Images Based on Multiclass Spatial-Spectral Generative Adversarial Networks.IEEE Transactions on Geoscience and Remote Sensing.
7. Feng, J., Chen, J., Liu, L., Cao, X., Zhang, X., Jiao, L., & Yu, T. (2019). CNN-Based Multil[ant]ayer Spatial–Spectral Feature Fusion and Sample Augmentation With Local and Nonlocal Constraints for Hyperspectral Image Classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
8. Zhao J, Ge Y, Cao X. Non-overlapping classification of hyperspectral imagery[J]. Remote Sensing Letters, 2019, 10(10): 968-977.
9. Cao X, Ji Y, Wang L, et al. SAR image change detection based on deep denoising and CNN[J]. IET Image Processing, 2019.
10.Cao X, Wang XZ, Wang D, Zhao J and Jiao LC.Spectral-Spatial Hyperspectral Image Classification Using Cascaded Markov RandomFields.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Accepted.
2018
1. Cao X, Li R, Wen L, et al. Deep Multiple Feature Fusion for Hyperspectral Image Classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3880-3891.
2. Cao X, Ji Y, Wang L, et al. Fast hyperspectral band selection based on spatial feature extraction[J]. Journal of Real-Time Image Processing, 2018, 15(3): 555-564.
3.Cao X, Ji Y, Liang T, et al. A semi-supervised spatially aware wrapper method for hyperspectral band selection[J]. International journal of remote sensing, 2018, 39(12): 4020-4039.
4.Feng, J., Liu, L., Cao, X., Jiao, L., Sun, T., & Zhang, X. (2018). Marginal stacked autoencoder with adaptively-spatial regularization for hyperspectral image classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,11(9), 3297-3311.
2017
1. Cao X, Wei C, Han J, et al. Hyperspectral band selection using improved classification map[J]. IEEE geoscience and remote sensing letters, 2017, 14(11): 2147-2151.
2. Cao X, Ji B, Ji Y, et al. Hyperspectral image classification based on filtering: a comparative study[J]. Journal of Applied Remote Sensing, 2017, 11(3): 035007.
3. Cao, X., Li, X., Li, Z., & Jiao, L.Hyperspectral band selection with objective image quality assessment.International Journal of Remote Sensing, 38(12), 3656-3668,2017.
4. Jiao L, Liang M, Chen H, Yang S, Liu H and Cao X. Deep fully convolutional network-based spatial distribution prediction for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(10): 5585-5599.
5. Zhao, W., Jiao, L., Ma, W., Zhao, J., Zhao, J., Liu, H., Cao, X. and Yang, S. Superpixel-Based Multiple Local CNN for Panchromatic and Multispectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing, 2017.
2016
1. Cao X, Wu B, Tao D, et al. Automatic band selection using spatial-structure information and classifier-based clustering[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4352-4360.
2. Cao X, Xiong T, Jiao L. Supervised band selection using local spatial information for hyperspectral image[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(3): 329-333.
3. Cao X, Han J, Yang S, et al. Band selection and evaluation with spatial information[J]. International journal of remote sensing, 2016, 37(19): 4501-4520.
~2015
1.曹向海, 邓湖明, 黄波. 背景感知的显著性检测算法[J]. 系统工程与电子技术, 2014(8):1668-1672.
2.曹向海, 贺浪, 张苹. 无训练样本的伪画像生成及识别[J]. 系统工程与电子技术, 2014, 36(1):194-197.
3.曹向海, 刘宏伟, 吴顺君. 多极化多特征融合的雷达目标识别研究[J]. 系统工程与电子技术, 2008, 30(2):261-264.
4.曹向海, 刘宏伟, 吴顺君. 基于在线Music算法的DOA估计[J]. 电子与信息学报, 2008, 30(11):2658-2661.
5.曹向海, 刘宏伟, 吴顺君. 数据加长和最近邻特征线分类器用于距离像识别[J]. 西安电子科技大学学报, 2007, 34(6):930-934.
6.曹向海, 刘宏伟, 吴顺君. 快速增量主分量算法的近似协方差矩阵实现[J]. 西安电子科技大学学报:自然科学版, 2010, 37(3):459-463.
7.曹向海, 刘宏伟, 吴顺君. 基于奇异值分解的双谱降维研究[J]. 宇航学报, 2007.
已授权发明专利：
1. 基于半监督的图像显著性检测方法，曹向海；焦李成；张丹；王爽；刘红英；马文萍；马晶晶，CNB。
2. 光学图像中存在显著目标的检测方法，曹向海；焦玲玲；杨淑媛，CNB。
3.基于CDCP局部描述子的遥感图像场景分类方法, 曹向海；李泽翰；李星华；梁甜；焦李成，CNB。
4.基于图像质量评价的高光谱图像波段选择方法，曹向海；李星华；梁甜；李泽翰；焦李成，CNB。
5.基于像素聚类的wrapper式高光谱波段选择方法，曹向海；焦李成；姚利；汪波棚；杨淑媛；刘红英；马晶晶；马文萍，CNB。
6.基于边界点重分类的高光谱图像分类方法，曹向海；焦李成；汪波棚；姚利；王爽；刘红英；马文萍；马晶晶，CNB。
研究生招生：
每年招收3-4名同学，最基本的要求是积极主动，另外需要你有点编程基础，如Matlab或Python，且具有较好的英语读写能力。
2019年刚刚毕业的四位研究生同学，其中三位同学发表或录用了2篇SCI，一位同学发表了一篇SCI.
期待你的加入！

科学研究
目前研究团队承担的科研项目：
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学术论文
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科研团队
团队教师

博士研究生
硕士研究生

课程教学
目前本人承担的教学任务：
高级语言程序设计；
离散数学；
多源信息融合；
Signals and Systems;
Digitial Signal Processing.

招生要求
关于研究生招生的信息：
招生专业： 电路与系统，电子与通信工程
每年招收3-4名硕士研究生。
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