涂兵教授

个人简介

涂兵，男，1983年1月生，湖南省岳阳人，IEEE Senior Member， 南京信息工程大学教授、博士生导师。湖南省杰青、湖南省青年骨干教师、岳阳巴陵青年英才、常州龙城英才(领军人才)、湖南省普通高校教师党支部书记“双带头人”，国家公派赴美研究学者。主持国家自然科学基金3项、湖南省杰出青年科学基金、湖南省水利厅重大科研项目、湖南省重点领域研究计划子课题等各类项目20余项，主要研究兴趣包括光谱信息处理、遥感图像处理、机器学习方法等，发表包括 IEEE Transactions on Neural Networks and Learning Systems、IEEE Transactions on Geoscience and Remote Sensing、IEEE Transactions on Instrumentation and Measurement、IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing在内的高水平论文60多篇，7篇论文入选ESI 1%高被引论文，获发明专利7项，获湖南省技术发明三等奖、教学成果三等奖各1项。担任IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing编委。

团队研究背景与研究方向

Accepted  

[62]Xiaolong Liao, Bing Tu*, Jun Li, Antonio Plaza. Class-wise Graph Embedding-based Active Learning for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2023, Doi: 10.1109/TGRS.2023.3309032

[61]Xianchang Yang, Bing Tu*, Qianming Li, Jun Li, Antonio Plaza. Graph Evolution-Based Vertex Extraction for Hyperspectral Anomaly Detection. IEEE Transactions on Neural Networks and Learning Systems. 2023, Doi: 10.1109/TNNLS.2023.3303273. GEVE(TNNLS)-Code.rar

[60]Bing Tu, Wangquan He, Qianming Li, Yishu Peng, Antonio Plaza. A New Context-Aware Framework for Defending Against Adversarial Attacks in Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2023, 61, 5505114:1-14

[59]Bing Tu, Qi Ren, Qianming Li, Wangquan He, Wei. He. Hyperspectral Image Classification Using a Superpixel–Pixel–Subpixel Multilevel Network. IEEE Transactions on Instrumentation and Measurement. 2023, 72, 5013616: 1-16

[58]Xianfeng Ou, Meng Wu, Bing Tu, Guoyun Zhang, Wujin Li. Multi-Objective Unsupervised Band Selection Method for Hyperspectral Images Classification. IEEE Transactions on Image Processing. 2023, 32: 1952-1965

[57]Yishu Peng, Yaru Liu, Bing Tu*, Yuwen Zhang. Convolutional Transformer-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023, 16: 1335-1349.  CTFSL(JSTARS)-Code.rar

[56]Yuwen Zhang, Yishu Peng , Bing Tu, Yaru Liu. Local Information Interaction Transformer for Hyperspectral and LiDAR Data Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2023, 16: 1130-1143

[55]Yishu Peng, Yuwen Zhang, Bing Tu*, Chengle Zhou, Qianming Li. Multi-View Hierarchical Network for Hyperspectral and LiDAR Data Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15: 1454-1469

[54]Bing Tu, Xianchang Yang, Wei He, Jun Li, Antonio Plaza. Hyperspectral Anomaly Detection Using Reconstruction Fusion of Quaternion Frequency Domain Analysis. IEEE Transactions on Neural Networks and Learning Systems. 2022, Doi: 10.1109/TNNLS.2022.3227167. RFFT-MsRFQFT-MfRFQFT(TNNLS)-Code.rar

[53]Bing Tu, Zhi Wang, Huiting Ouyang, Xianchang Yang, Jun Li, Antonio Plaza. Hyperspectral Anomaly Detection Using the Spectral-Spatial Graph. IEEE Transactions on Geoscience and Remote Sensing. 2022, 60, 5542814:1-14. SSG(TGRS)_Code.rar

[52]Bing Tu, Xiaolong Liao, Qianming Li, Yishu Peng, Antonio Plaza. Local Semantic Feature Aggregation-Based Transformer for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2022, 60, 5536115:1-15. LSFAT(TGRS)-Code.rar

[51]Bing Tu, Xianchang Yang, Xianfeng Ou, Guoyun Zhang, Jun Li, Antonio Plaza. Ensemble Entropy Metric for Hyperspectral Anomaly Detection. IEEE Transactions on Geoscience and Remote Sensing. 2022, 60, 5513617:1-17. EEMD(TGRS)-Code.rar

[50]Bing Tu, Chengle Zhou, Xiaolong Liao, Qianming Li, Yishu Peng. Feature Extraction via 3-D Block Characteristics Sharing for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021, 59(12): 10503-10518

[49]Bing Tu, Chengle Zhou, Danbing He, Siyuan Huang, Antonio Plaza. Hyperspectral Classification with Noisy Label Detection via Superpixel-to-Pixel Weighting Distance. IEEE Transactions on Geoscience and Remote Sensing. 2020, 58(6): 4116-4131

[48]Bing Tu, Xianchang Yang, Chengle Zhou, Danbing He, Antonio Plaza. Hyperspectral Anomaly Detection Using Dual Window Density. IEEE Transactions on Geoscience and Remote Sensing. 2020, 58(12): 8503-8517.RDWD(TGRS)-Code.rar

[47]Bing Tu, Xiaofei Zhang, Xudong Kang, Guoyun Zhang, Shutao Li. Density Peak-based Noisy Label Detection for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2019, 57(3): 1573-1584

[46]Bing Tu, Xiaofei Zhang, Xudong Kang, Jinping Wang, Jon Alti Benediktsson. Spatial Density Peak Clustering for Hyperspectral Image Classification with Noisy Labels. IEEE Transactions on Geoscience and Remote Sensing. 2019, 57(7): 5085-5097

[45]Bing Tu, Zhi Wang, Xianchang Yang, Jun Li, Antonio Plaza. Hyperspectral Anomaly Detection Using Quantum Potential Clustering. IEEE Transactions on Instrumentation and Measurement. 2022, 71, 5025913: 1-13. QPAD(TIM)-Code.rar

[44]Bing Tu, Chengle Zhou, Wenlan Kuang, Siyuan Chen, Antonio Plaza. Multiattribute Sample Learning for Hyperspectral Image Classification Using Hierarchical Peak Attribute Propagation. IEEE Transactions on Instrumentation and Measurement. 2022, 71, 6502617:1-17

[43]Bing Tu, Wangquang He, Qianming Li, Yishu Peng, Siyuan Chen. Fully Convolutional Network-Based Nonlocal-Dependent Learning for Hyperspectral Image Classification. IEEE Transactions on Instrumentation and Measurement. 2022, 71, 5023414:1-14

[42]Bing Tu, Chengle Zhou, Jin Peng, Guoyun Zhang, Yishu Peng. Feature Extraction via Joint Adaptive Structure Density for Hyperspectral Imagery Classification. IEEE Transactions on Instrumentation and Measurement. 2021, 70, 5006916:1-16

[41]Bing Tu, Xiaolong Liao, Chengle Zhou, Siyuan Chen, Wei He. Feature Extraction Using Multitask Superpixel Auxiliary Learning for Hyperspectral Classification. IEEE Transactions on Instrumentation and Measurement. 2021, 70, 5018216:1-16

[40]Yishu Peng, Yuwen Zhang, Bing Tu*, Qianming Li, Wujin Li. Spatial-Spectral Transformer With Cross-Attention for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2022, 60, 5537415:1-15

[39]Xianfeng Ou, Liangzhen Liu, Bing Tu*, Guoyun Zhang, Zhi Xu. A CNN Framework With Slow-Fast Band Selection and Feature Fusion Grouping for Hyperspectral Image Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 2022,60, 5524716:1-16

[38]Bing Tu, Xiaolong Liao, Qianming Li, Chengle Zhou, Antonio Plaza. Multi-resolution Pyramid Nonlocal Enhanced Feature Extraction for Hyperspectral Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15: 5865 – 5879

[37]Bing Tu, Wangquan He, Wei He, Xianfeng Ou, Antonio Plaza. Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15, 184-200

[36]Qi Ren, Bing Tu*, Qianming Li, Wangquan He, Yishu Peng. Multiscale Adaptive Convolution for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15: 5115 - 5130

[35]Bing Tu, Yu Zhu, Chenle Zhou, Siyuan Chen, Antonio Plaza. Optimized Spatial Gradient Transfer for Hyperspectral-LiDAR Data Classification. Remote Sensing. 2022, 14(8), 1814:1-22

[34]Qi Ren, Bing Tu*, Sha Liao, Siyuan Chen. Hyperspectral Image Classification with IFormer Network Feature Extraction. Remote Sensing. 2022, 14, 4866:1-21

[33]Yishu Peng, Yuwen Zhang, Bing Tu*, Chengle Zhou, Qianming Li. Multiview Hierarchical Network for Hyperspectral and LiDAR Data Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2022, 15, 1454-1469

[32]Bing Tu, Chengle Zhou, Xiaolong Liao, Guoyun Zhang, Yishu Peng. Spectral–Spatial Hyperspectral Classification via Structural-Kernel Collaborative Representation. IEEE Geoscience and Remote Sensing Letters. 2021, 18(5): 861-865

[31]Chengle Zhou, Bing Tu*, Qi Ren, Siyuan Chen. Spatial Peak-Aware Collaborative Representation for Hyperspectral Imagery Classification. IEEE Geoscience and Remote Sensing Letters. 2022, 19, 5506805: 1-5

[30]Bing Tu, Qi Ren, Chengle Zhou, Siyuan Chen, Wei He. Multi-Dimensional Spectral Regression Whitening Feature Extraction for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 8326-8340

[29]Wenlan Kuang, Bing Tu*, Wangquan He, Guoyun Zhang, Yishu Peng. A Spectral-spatial Attention Aggregation Network for Hyperspectral Imagery Classification. International Journal of Remote Sensing. 2021.42(19):7551-7580

[28]Chengle Zhou, Bing Tu*, Nanying Li, Wei He, Antonio Plaza. Structure-Aware Multi-Kernel Learning for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021, 14: 9837-9854

[27]Bing Tu, Wenlan Kuang, Wangquan He, Guoyun Zhang, Yishu Peng. Robust Learning of Mislabeled Training Samples for Remote Sensing Image Scene Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, 13: 5623-5639

[26]Bing Tu, Chengle Zhou, Xiaolong Liao, Zhi Xu, Yishu Peng, Xianfeng Ou. Hierarchical Structure-Based Noisy Labels Detection for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, 13: 2183-2199

[25]Guoyun Zhang, Nanying Li, Bing Tu*, Zhuolang Liao, Yishu Peng. Hyperspectral Anomaly Detection via Dual Collaborative Representation, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, 13: 4881-4894

[24]Qianming Li, Bohong Zheng, Bing Tu*, Yusheng Yang, Jiawei Yang. Refining Urban Built-Up Area via Multi-Source Data Fusion for the Analysis of Dongting Lake Eco-Economic Zone Spatiotemporal Expansion. Remote Sensing. 2020, 12(11):1797

[23]Bing Tu, Jinping Wang, Guangzhe Zhao, Xiaofei Zhang, Guoyun Zhang. Dual-Stage Construction of Probability for Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2020, 17(5): 889-893

[22]Guangzhe Zhao, Nanying Li, Bing Tu*, Guoyun Zhang, Wei He. Density Peak Covariance Matrix for Feature Extraction of Hyperspectral Image. IEEE Geoscience and Remote Sensing Letters. 2020, 17(3): 534-538

[21]Bing Tu, Xianchang Yang, Nanying Li, Chengle Zhou, Danbing He. Hyperspectral Anomaly Detection via Density Peak Clustering. Pattern Recognition Letters. 2020, 129: 144-149

[20]Bing Tu, Xiaofei Zhang, Guoyun Zhang, Jinping Wang, Wei He. Dual unsupervised features fusion for hyperspectral image classification. International Journal of Remote Sensing. 2020, 41(16):6135-6156.

[19]Bing Tu, Jinping Wang, Guoyun Zhang, Xiaofei Zhang, Wei He. Texture Pattern Separation for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019, 12(9): 3602-3614

[18]Bing Tu, Xiaofei Zhang, Jinping Wang, Zhuolang Liao, Jin Peng. Noisy Labels Detection in Hyperspectral Image via Class-Dependent Collaborative Representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2019, 12(12): 5076-5085

[17]Bing Tu, Nanying Li, Leyuan Fang, Danbing He, Pedram Ghamisi. Hyperspectral Image Classification with Multi-scale Feature Extraction. Remote Sensing. 2019, 11(5), 534: 1-16

[16]Bing Tu, Nanying Li, Zhuolang Liao, Xianfeng Ou, Guoyun Zhang. Hyperspectral Anomaly Detection via Spatial Density Background Purification. Remote Sensing. 2019, 11(22), 2618: 1-20

[15]Bing Tu, Chengle Zhou, Jin Peng, Wei He, Xianfeng Ou, Zhi Xu. Kernel Entropy Component Analysis-Based Robust Hyperspectral Image Supervised Classification. Remote Sensing. 2019, 11(23), 2823: 1-25

[14]Bing Tu, Nanying Li, Leyuan Fang, Xianchang Yang, Jianhui Wu. Hyperspectral Image Classification with Class-dependent Spatial-spectral Mixed Metric. Pattern Recognition Letters. 2019, 123: 16-22

[13]Bing Tu, Wenlan Kuang, Guangzhe Zhao, Danbing He, Zhuolang Liao, Weiwen Ma. Hyperspectral Image Classification via Combining Local Binary Pattern and Joint Sparse Representation. International Journal of Remote Sensing. 2019, 40(24): 9484-9500

[12]Bing Tu, Wenlan Kuang, Chengle Zhou, Guoyun Zhang, Binxin Zheng. Hyperspectral Image Classification using Spectral Mixing Metrics Representation. Remote Sensing Letters. 2019, 10(4): 391-400

[11]Guangzhe Zhao, Bing Tu*, Hongyan Fei, Nanying Li, Xianchang Yang. Spatial-spectral Classification of Hyperspectral image via Group Tensor Decompositon. Neurocomputing. 2018, 316: 68-77

[10]Bing Tu, Jinping Wang, Xudong Kang, Guoyun Zhang, Xianfeng Ou, Longyuan Guo. KNN-based Representation of Superpixels for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018, 11(11): 4032-4047

[9]Bing Tu, Siyuan Huang, Leyuan Fang, Guoyun Zhang, Jinping Wang, Binxin Zheng. Hyperspectral Image Classification via Weighted Joint Nearest Neighbor and Sparse Representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018, 11(11): 4063-4075

[8]Bing Tu, Xianchang Yang, Nanying Li, Xianfeng Ou, Wei He. Hyperspectral Image Classifiction via Superpixel Correlation Coefficient Representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018, 11(11): 4113-4127

[7]Bing Tu, Chengle Zhou, Wenlan Kuang, Longyuan Guo, Xiangfeng Ou. Hyperspectral Imagery Nosiy Label Detection by Spectral Angel Local Outlier Factor. IEEE Geoscience and Remote Sensing Letters. 2018, 15(9): 1417-1421

[6]Bing Tu, Xiaofei Zhang, Xudong Kang, Guoyun Zhang, Jinping Wang, Jianhui Wu. Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation. IEEE Geoscience and Remote Sensing Letters. 2018, 15(3): 340-344

[5]Bing Tu, Wenlan Kuang, Guangzhe Zhao, Hongyan Fei. Hyperspectral Image Classification via Superpixel Spectral Metrics Representation. IEEE Signal Processing Letters. 2018, 25(10): 1520-1524.

[4]Bing Tu, Nanying Li, Leyuan Fang, Hongyan Fei, Danbing He. Classification of Hyperspectral Images via Weighted Spatial Correlation Representation. Journal of Visual Communication and Image Representation. 2018, 56: 160-166

[3]Qianming Li, Bohong Zheng, Bing Tu*, Jinping Wang, Chengle Zhou. Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020, 13: 5134-5148

[2]Guoyun Zhang, Jinping Wang, Xiaofei Zhang, Hongyan Fei, Bing Tu*. Adaptive Total Variation-based Spectral-Spatial Feature Extraction of Hyperspectral Image. Journal of Visual Communication and Image Representation. 2018, 56: 152-159

[1]Bing Tu, Xiaofei Zhang, Jinping Wang, Guoyun Zhang, Xianfeng Ou.Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction. Sensing and Imaging. 2018, 19(1): 11:1-25

 代表性科研项目

1. 国家自然科学基金面上项目，跨场景湿地高光谱遥感图像域自适应分类方法及动态监测应用，62271200，起止时间：2023.1-2026.12，主持，在研

2. 国家自然科学基金面上项目，湿地高光谱遥感图像小/不确定/多标签样本分类方法及动态监测应用研究，61977022，起止时间：2020.1-2023.12，主持，在研

3. 国家自然科学基金青年项目，基于改进集合经验模态分解与稀疏表示的连续钻井液压力波信号处理方法研究，51704115，起止时间：2018.1-2020.12，主持，结题

4. 湖南省杰出青年科学基金，湿地高光谱遥感图像分类方法及动态监测应用研究，2020JJ2017，起止时间：2020.1-2022.12，主持，结题

5. 湖南省水利厅重大科技项目，基于高光谱热红外的堤防安全隐患快速探测技术及洞庭湖应用示范，XSKJ2021000-13，起止时间：2021.9-2023.12，主持，在研

6. 湖南省重点领域研究计划，新时代国土空间规划关键技术研究与示范，2019SK2012，起止时间：2019.9-2022.12，主持，结题

7. 湖南省自然科学基金青年项目，面向湖泊水域动态监测的高光谱遥感图像深层空谱特征提取分类算法研究，2019JJ50212，起止时间：2019.1-2021.12，主持，结题

8. 湖南省教育厅优秀青年项目，高光谱遥感图像中河流提取与类型识别方法及洞庭湖流域应用研究，20B257，起止时间：2020.1-2022.12，主持，结题

教育经历

暂无内容

工作经历

暂无内容

社会兼职

• 暂无内容

研究方向

其他联系方式

团队成员

暂无内容