导师姓名：赵永强
性别：
人气指数：46

所属院校：西北工业大学
所属院系：自动化学院
职称：教授
导师类型：
招生专业：控制科学与工程


通讯方式 :
办公电话：**
电子邮件：zhaoyq@nwpu.edu.cn


个人简述 :
2003年?国防科工委科技进步二等奖2010年?陕西省科学技术进步二等奖2014年?陕西省科学技术进步二等奖2012年?入选教育部“新世纪优秀人才支持计划”

科研工作 :
科研方向:图像处理，机器视觉，偏振成像，高光谱遥感，光电探测等[1] 仿生偏振视觉偏振视觉能够准确的获取散射介质中的感兴趣目标的各种信息，是机器视觉领域一个新的研究方向。主要开展高速、高分辨微纳光栅阵列偏振成像、多波段偏振联合编解码、仿生多波段偏振视觉模型、多波段偏振视觉信息分析、仿生偏振光组合导航等方面的工作。[2] 高维图像重构通过后期处理来获取高空间、光谱以及偏振分辨率的图像是偏振光谱遥感发展的重要方向之一，同时也对传统图像重构理论产生了全新的挑战。在该领域压缩偏振光谱成像联合稀疏采样、多波段图像超分辨重构、计算光谱成像、多目标联合优化等方面的研究。[3] 多波段光电探测系统主要在可见光、红外、多光谱、偏振图像目标检测、跟踪、识别等方向开展研究工作，结合工程实际设计不同的成像系统。科研项目:[1] 国家自然科学基金 (NSFC：**), “基于光栅阵列和深度学习的偏振光谱成像理论研究”. 1/2018?—12/2021(负责人)[2] 国家自然科学基金委员会与韩国国家研究基金会联合资助合作交流项目（NSFC- NRF: **），“基于微纳滤光片级联阵列的多波段偏振成像理论研究”.7/2015 — 6/2017(负责人)[3] 国家自然科学基金 (NSFC：**), “用于压缩偏振光谱成像的联合稀疏采样理论研究”. 1/2014 —12/2017(负责人)[4] 国家自然科学基金 (NSFC：**), “仿生多波段偏振视觉感知模型研究”. 1/2011 —12/2013(负责人)[5]国家自然科学基金 (NSFC：**), “基于多尺度分析的成像光谱偏振探测信息综合及应用”. 1/2007 —12/2009(负责人) 专著：[1]赵永强，潘泉，程咏梅. 成像偏振光谱遥感及应用.国防工业出版社(国防科技图书出版基金资助).2011年5月. （书中所涉及的部分数据下载：多光谱偏振融合数据、偏振光谱图像测和分类数据、偏振二向反射数据1、偏振二向反射数据2）[2] Yongqiang Zhao, Quan Pan,S.G.Kong, Chen Yi, Yongmei Cheng. Multi-band Optical Polarization Imaging and Application. Springer.2016.(PDF)期刊论文:[37]Jize Xue,Yongqiang Zhao?and Wenzhi Liao et al. Nonlocal Low-rank Regularized Tensor Decomposition for Hyperspectral Image Denoising. Submitted.(paper,code)[36]Jize Xue,Yongqiang Zhao?and Wenzhi Liao et al. Nonconvex Tensor Rank Minimization and Its Application for Tensor Data.Submitted.(paper,code)[35] Mohamed Reda, Yongqiang Zhao and Jonathan C-W Chan. Matching enhancement using polarization and depth information.Submitted.(paper,code)[34] Jingxiang Yang, Yongqiang Zhao,Jonathan Cheung-Wai Chan. Hyperspectral and Multispectral Image Fusion via Deep Two-Branches Convolutional Neural Network.Submitted.(paper,code)[33] Chen Yi,Yongqiang Zhao,J.W. Chan. Hyperspectral image super-resolution based on spatial and spectral correlation fusion. Submitted.(paper,code)[32] Yongqiang Zhao,Shen Lin Hao,?Qunnie Peng. Robust Image Dehazing With Polarization and Noise Suppression. Submitted.(paper,code)[31]Yongqiang Zhao,Hao Jinglei,?Qunnie Peng. Straylights Suppression of High-reflective Objects Based on Multiband Polarization Imaging.?Submitted.?(paper,code)[30] Yongqiang Zhao,Miaomiao Wang,?J.W. Chan. FOV Expansion of Bio-Inspired Multiband Polarimetric Imagers with Convolutional Neural Networks. IEEE Photonics Journal.2018.?(paper,code)[29] Xixi Ping, Yong Liu, Yongqiang Zhao?et al. 3-D reconstruction of textureless and high-reflective target by polarization?and binocular stereo vision. Journal of Infrared and Millimeter Waves.?2017,36(4):432-438(paper,code)[28]Jize Xue, Yongqiang Zhao, Wenzhi Liao, and Seong G. Kong.Joint Spatial and Spectral Low-Rank Regularization for Hyperspectral Image Denoising.?IEEE Trans. Geoscience and Remote?Sensing.2017.(paper,code)[27] Lin Li, Yongqiang Zhao, Jinjun Sun, et. Al. Deformable Dictionary Learning for SAR Image Change Detection. IEEE Trans. Geoscience and Remote?Sensing.2017. (paper,code)[26]Mohamed Reda, Yongqiang Zhao and Jonathan C-W Chan. Polarization guided auto-regressive model for depth recovery. IEEE Photonics Journal.2017.(paper,code)[25] Jingxiang Yang, Yongqiang Zhao,Jonathan Cheung-Wai Chan. Learning and Transferring Deep Joint Spectral-Spatial Feature for Hyperspectral Classification.IEEE Trans. Geoscience and Remote?Sensing.2017. (paper,code)[24]Jingxiang Yang, Yongqiang Zhao, Chen Yi and Jonathan Cheung-Wai Chan. No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning. Remote Sensing.2017(paper,code) [23] Chen Yi, Yongqiang Zhao, Jingxiang Yang, Jonathan Cheung-Wai Chan, and Seong G. Kong, Joint Hyperspectral Super-Resolution and Unmixing with Interactive Feedback. IEEE Trans. Geoscience and Remote?Sensing.2017. (paper,code)[22] Jingxiang Yang, Yongqiang Zhao, JCW Chan, SG Kong. Coupled Sparse Denoising and Unmixing with Low Rank Constraint for Hyper-spectral Image. IEEE Trans. Geoscience and Remote Sensing.?2016.(paper,code)[21] Jinhuan Wen, James E. Fowler, Mingyi He, Yongqiang Zhao, Chengzhi Deng, and Vineetha Menon. Orthogonal Nonnegative Matrix Factorization Combining Multiple Features for Hyerspectral Image Spectral-Spatial Dimension Reduction.IEEE Trans. Geoscience and Remote Sensing. 2016.(paper,code)[20] Yongqiang Zhao, Qunnie Peng, Chen Yi and Seong G. Kong. Multi-band Polarization Imaging. Journal of Sensors. 2016.(paper,code)[19] Yongqiang Zhao, Jingxiang Yang. Hyperspectral Image Denoising via Sparse Representation and Low-Rank Constraint. IEEE Trans. on Geoscience and Remote Sensing, 53(1):2015.296-308.(PDF,Code)[18] Yongqiang Zhao, Jingxiang Yang, JCW Chan. Hyperspectral Imagery Super-Resolution by Spatial–Spectral Joint Nonlocal Similarity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1-13, 2014.(PDF)[17] S.B.Gao, Y.M. Cheng, Y.Q. Zhao et al. Data-driven quadratic correlation filter using sparse coding for infrared targets detection. Journal of Infrared and Millimeter Waves.?,2014,33(5):498-506[16] S Gao, Y Cheng, Yongqiang Zhao. Method of visual and infrared fusion for moving object detection. Optics letters 38 (11), 1981-1983, 2013.(PDF)[15] Yongqiang Zhao, SG Kong. Automated classification of touching or overlapping M-FISH chromosomes by region fusion and homolog pairing. Pattern Analysis and Applications 16 (1), 31-39, 2013.(PDF)[14] S Gao, Y Cheng, Yongqiang Zhao. Unsupervised change detection of satellite images using low rank matrix completion. Optics Letters 38 (23), 5146-5149,2013. (paper)[13] Jinhuan Wen, Yongqiang Zhao, Xingfu Zhang, Weidong Yan, Wei Lin, Local discriminant nonnegative matrix factorization feature extraction for hyperspectral image classification, International Journal of Remote Sensing, 2014, 35(13): 5073–5093.(paper)[12] Yongqiang Zhao, Q Zhang, J Yang. High-resolution multiband polarization epithelial tissue imaging method by sparse representation and fusion. Applied Optics 51 (4), 2012:A27-A35. (paper)[11] Yongqiang Zhao, L Zhang, SG Kong. Band-subset-based clustering and fusion for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49 (2), 747-756.?(paper)[10] Yongqiang Zhao, G Zhang, F Jie, S Gao, C Chen, Q Pan. Unsupervised Classification of Spectropolarimetric Data by Region-Based Evidence Fusion. IEEE Geoscience and Remote Sensing Letters, 2011, 755-759.(paper)[8] Yongqiang Zhao, J Yang, Q Zhang, L Song, Y Cheng, Q Pan. Hyperspectral imagery super-resolution by sparse representation and spectral regularization. EURASIP Journal on Advances in Signal Processing, 2011 (1), 87. (paper)[8] Yongqiang Zhao, X Wu, SG Kong, L Zhang. Joint segmentation and pairing of multispectral chromosome images. Pattern Analysis & Applications, 2011:1-10.(Data, paper)[7 ] Y. Wu, Y.M. Cheng, Y.Q. Zhao et al. Infrared target detection using kernel Rayleigh quotient quadratic correlation filter. Journal of Infrared and Millimeter Waves.?,2011,30(2):142-148[6] C. Chen, Y.Q. Zhao, L. Luo et al. Model and Analysis of Spectropolarimetric BRDF of Painted Target Based on GA-LM Method. Spectroscopy and Spectral Analysis. 2010: 729-734.[5] Yongqiang Zhao, Peng Gong, Quan Pan. Object Detection by Spectropolarieteric Imagery Fusion, IEEE Transactions on Geoscience and Remote Sensing, 46(9), 2008.(paper)[4] Yongqiang Zhao, Lei Zhang, Quan Pan. Spectropolarimetric imaging for pathological analysis of skin, Applied Optics, 48(10), 2009: D236-D246.(paper)[3] Yongqiang Zhao, Lei Zhang, Quan Pan. Object Separation by Polarimetric and Spectral Imagery Fusion, Computer Vision and Image Understanding, 2009.(paper)[2] S.B.Gao, Y.M. Cheng, Y.Q. Zhao et al. Detection of Buried Target Based on Multitemporal Infrared Image. Journal of Infrared and Millimeter Waves.?2009,28(1):142-148[1] Yongqiang Zhao, Quan Pan, Hong-Cai Zhang. New Polarization Imaging Method based on Spatially Adaptive Wavelet Image Fusion, Optical Engineering,45(12),?2006: 123202 -1-6.(paper)