学术研究
 当前位置： 关于我们 > 学术研究 >

学术研究

期刊论文
  • [1] H. Zhai, H. Zhang*, P. Li, L. Zhang, "Hyperspectral Image Clustering: Current Achievements and Future Lines", IEEE Geoscience and Remote Sensing Magazine, DOI: 10.1109/MGRS.2020.3032575, 2021.
  • [2] S. Huang, H. Zhang*, A. Pižurica, "Hybrid-hypergraph Regularized Multi-view Subspace Clustering for Hyperspectral Images", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2021.3074184, 2021.
  • [3] H. Zhang, J. Cai, W. He, H. Shen, L. Zhang, "Double Low-Rank Matrix Dcomposition for Hyperspectral Image Denoising and Destriping", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2021.3061148, 2021.
  • [4] H. Xu, H. Zhang*, L. Zhang, "A Superpixel Guided Sample Selection Neural Network for Handling Noisy Labels in Hyperspectral Image Classification", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3040879, 2020.
  • [5] H. Zhai, H. Zhang*, L. Zhang, P. Li, "Sparsity-based Clustering for Large Hyperspectral Remote Sensing Images", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3032427, 2020.
  • [6] H. Zhang, Y. Liao, H. Yang, G. Yang, L. Zhang, "A Local-Global Dual-Stream Network for Building Extraction from Very High Resolution Remote Sensing Images", IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2020.3041646, 2020.
  • [7] H. Zhang*, H. Du, C. Zhang, L. Zhang "An Automated Early-season Method to Map Winter Wheat using Time-series Sentinel-2 Data: A Case Study of Shandong, China", Computers and Electronics in Agriculture, vol. 182, Article ID: 105962, 2021.
  • [8] C. Zhang, H. Zhang*, L. Zhang, "Spatial Domain Bridge Transfer：An Automated Paddy Rice Mapping Method with no Training Data Required and Decreased Image Inputs for Large Cloudy Area", Computers and Electronics in Agriculture, vol. 181, Article ID: 105978, 2021.
  • [9] W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao, H. Zhang, L. Zhang, "Non-local Meets Global: An Integrated Paradigm for Hyperspectral Image Restoration", IEEE Transactions on Pattern Analysis and Machine Intelligence, DOI: 10.1109/TPAMI.2020.3027563, 2020.
  • [10] S. Kunwar, H. Chen, M. Lin, H. Zhang, P. D'Angelo, D. Cerra, S. M. Azimi, M. Brown, G. Hager, N. Yokoya, R. Hansch, and B. Le Saux, "Large-Scale Semantic 3D Reconstruction: Outcome of the 2019 IEEE GRSS Data Fusion Contest - Part A", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 922 - 935, 2020.
  • [11] Y.-Y. Liu, X.-L. Zhao, Y.-B. Zheng, T.-H. Ma, H. Zhang, "Hyperspectral Image Restoration by Tensor Fibered Rank Constrained Optimization and Plug-and-Play Regularization", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3045169, 2020.
  • [12] H. Zhang, Y. Song, C. Han, L. Zhang, "Remote Sensing Image Spatiotemporal Fusion Using a Generative Adversarial Network", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3010530, 2020.(PDF)
  • [13] H. Zhai, H. Zhang*, L. Zhang, P. Li, "Non-Local Means Regularized Sketched Reweighted Sparse and Low-Rank Subspace Clustering for Large Hyperspectral Images", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2020.3023418, 2020.
  • [14] H. Zhang, L. Liu, W. He, L. Zhang, "Hyperspectral Image Denoising with Total Variation Regularization and Nonlocal Low-Rank Tensor Decomposition", IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3071-3084, 2020.(PDF)
  • [15] H. Zhang, J. Kang, Xiong Xu, L. Zhang, "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", Computers and Electronics in Agriculture, vol. 176, Article ID: 105618, 2020.(PDF)
  • [16] X. R. Feng, H. C. Li, S. Liu, H. Zhang, "Correntropy-based Autoencoder-like NMF with Total Variation for Hyperspectral Unmixing", IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2020.3020896, 2020.(PDF)
  • [17] Z. Xue, S. Yang, H. Zhang, P. Du, "Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification", Remote Sensing, vol. 11, no. 17, DOI: 10.3390/rs11171959, 2019.(PDF)
  • [18] S. Huang, H. Zhang, A. Pižurica, "Sketch-based Subspace Clustering of Hyperspectral Images", Remote Sensing, vol. 12, no. 5, DOI: 10.3390/rs12050775, 2020.(PDF)
  • [19] H. Chen, H. Zhang, J. Du, B. Luo, "Unified Framework for the Joint Super-Resolution and Registration of Multiangle Multi/Hyperspectral Remote Sensing Images", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, no. 1, pp. 2369-2384, 2020.(PDF)
  • [20] X. Meng, Y. Xiong, F. Shao, H. Shen, W. Sun, G. Yang, Q. Yuan, R. Fu, H. Zhang, "A Large-Scale Benchmark Data Set for Evaluating Pansharpening Performance: Overview and implementation", IEEE Geoscience and Remote Sensing Magazine, DOI: 10.1109/MGRS.2020.2976696, 2020.(PDF)
  • [21] H. Xu, H. Zhang, W. He, and L. Zhang, “Superpixel-based spatial-spectral dimension reduction for hyperspectral imagery classification,” Neurocomputing, vol. 360, pp. 138 – 150, 2019.(PDF)
  • [22] S. Huang, H. Zhang, A. Pižurica, "Semi-supervised Sparse Subspace Clustering Method With a Joint Sparsity Constraint for Hyperspectral Remote Sensing Images", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 3, pp. 989-999, 2019.(PDF)
  • [23] H. Zhai, H. Zhang*, L. Zhang, P. Li, "Total Variation Regularized Collaborative Representation Clustering with a Locally Adaptive Dictionary for Hyperspectral Imagery", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2018.2852708, 2018.(PDF)
  • [24] H. Zhai, H. Zhang*, L. Zhang*, P. Li, "Cloud/Shadow Detection Framework based on Spectral Indices for Multi/Hyperspectral Optical Remote Sensing Imagery", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 144, pp.235-253,2018.(PDF)
  • [25] H. Zhai, H. Zhang*, L. Zhang, P. Li, "Laplacian-Regularized Low-Rank Subspace Clustering for Hyperspectral Image Band Selection", IEEE Transactions on Geoscience and Remote Sensing, DOI: 10.1109/TGRS.2018.2868796, 2018.
  • [26] W. He, H. Zhang*, H. Shen, L. Zhang*, "Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 3, pp. 713 - 729, 2018.(PDF)
  • [27] Y. Zhang, P. Jiang, H. Zhang, P. Cheng, "Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine", International Journal of Environmental Research and Public Health, vol. 15, no. 2, DOI: 10.3390/ijerph15020186, 2018.(PDF)
  • [28] H. Shao, H. Zhang, A. Pižurica, "A Robust Sparse Representation Model for Hyperspectral Image Classification", Sensors, vol. 17, no. 9, DOI: 10.3390/s17092087, 2017.(PDF)
  • [29] H. Fan, Y. Chen, Y. Guo, H. Zhang, G. Kuang, "Hyperspectral Image Restoration Using Low-Rank Tensor Recovery", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.1109/JSTARS.2017.2714338, 2017.(PDF)
  • [30] W. He, H. Zhang*, L. Zhang, H. Shen, "Total Variation Regularized Reweighted Sparse Non-Negative Matrix Factorization for Hyperspectral Unmixing",IEEE Trans. on Geoscience and Remote Sensing, vol. 55, no. 7, pp. 3909 - 3921, 2017.(PDF)
  • [31]H. Zhai, H. Zhang*, X. Xu*, L. Zhang, P. Li, "Kernel Sparse Subspace Clustering With a Spatial Max Pooling Operation for Hyperspectral Remote Sensing Data Interpretation", Remote Sensing, vol. 9, no. 4, DOI: 10.3390/rs9040335, 2017.(PDF)
  • [32] R. Luo, W. Liao, H. Zhang, L. Zhang, Y. Pi, P. Scheunders and W. Philips, "Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote Sensing Scene", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, DOI: 10.1109/JSTARS. 2017.2684085, 2017.(PDF)
  • [33] C. Han, N. Sang, H. Zhang, L. Zhang, "Gradient Transferred Pansharpening Method Based on Cosparse Analysis Model", Journal of Applied Remote Sensing, DOI: 10.1117/1.JRS.11.025009, 2017.(PDF)
  • [34] H. Zhai, H. Zhang*, L. Zhang, P. Li, A. Plaza, "A New Sparse Subspace Clustering Algorithm for Hyperspectral Remote Sensing Imagery", IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 1, pp. 43 - 47, 2017.(PDF)
  • [35] H. Zhai, H. Zhang*, L. Zhang, P. Li, "Reweighted Mass Center based Object-Oriented Sparse Subspace Clustering for Hyperspectral Images", Journal of Applied Remote Sensing, vol. 10, no. 4, Article ID: 046014, 2016.(PDF)
  • [36] L. Yue, H. Shen, J. Li, Q. Yuan, H. Zhang, L. Zhang, "Image super-resolution: the techniques, applications, and future", Signal Processing, vol. 128, pp. 389–408, 2016. (ESI Hot Paper)(PDF)
  • [37] X. Meng, J. Li, H. Shen, L. Zhang, H. Zhang, "Pansharpening with a Guided Filter Based on Three-Layer Decomposition", Sensors, vol. 16, no. 7, DOI:10.3390/s16071068, 2016.(PDF)
  • [38] H. Zhang, H. Zhai, L. Zhang, P. Li, "Spectral-Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images", IEEE Trans. on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3672–3684, June 2016.(PDF)
  • [39] W. He, H. Zhang*, L. Zhang, "Sparsity-Regularized Robust Non-Negative Matrix Factorization for Hyperspectral Unmixing", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4267 - 4279, 2016.(PDF)
  • [40] C. Han, H. Zhang*, C. Gao, C. Jiang, N. Sang, L. Zhang, "A Remote Sensing Image Fusion Method Based on the Analysis Sparse Model", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 1, pp. 439 - 453, 2016.(PDF)
  • [41] W. He, H. Zhang*, L. Zhang, W. Philips, W. Liao, "Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images", IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 5, pp. 686 - 690, 2016.(PDF)
  • [42] C. Jiang, H. Zhang*, L. Zhang, H. Shen, Q. Yuan, "Hyperspectral Image Denoising with a Combined Spatial and Spectral Hyperspectral Total Variation Model", Canadian Journal of Remote Sensing, vol. 42, no. 1, pp. 53 - 72, 2016.(PDF)
  • [43] W. He, H. Zhang*, L. Zhang, H. Shen, "Total-Variation-Regularized Low-rank Matrix Factorization for Hyperspectral Image Restoration", IEEE Trans. on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 178 - 188, 2016. (ESI Highly Cited Paper)(PDF)
  • [44] W. He, H. Zhang*, L. Zhang, H. Shen, "[15] J. Li, H. Zhang*, M. Guo, L. Zhang, H.Shen and Q. Du, "Urban Classification by the Fusion of Thermal Infrared Hyperspectral and Visible Data", Photogrammetric Engineering & Remote Sensing, vol. 81, no. 12, pp. 901–911. 2015 .(PDF)
  • [45] J. Li, H. Zhang*, L. Zhang, "Efficient Superpixel-level Multi-task Joint Sparse Representation for Hyperspectral Image Classification", IEEE Trans. on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5338-5351, 2015.(PDF)
  • [46] W. He, H. Zhang*, L. Zhang, H. Shen, "Hyperspectral Image Denoising via Noise-Adjusted Iterative Low-Rank Matrix Approximation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 3050 - 3061, 2015.(PDF)
  • [47] J. Li, H. Zhang*, L. Zhang, "A Nonlinear Multiple Features Learning Classifier for Hyperspectral Image with Limited Training Samples", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2728 - 2738, 2015.(PDF)
  • [48] J. Li, H. Zhang*, L. Zhang, L. Ma, "Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 6, pp. 2523 - 2533, 2015.(PDF)
  • [49] X. Ma, H. Shen, L. Zhang, J. Yang, H. Zhang, "Adaptive Anisotropic Diffusion Method for Polarimetric SAR Speckle Filtering", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 3, pp. 1939-1404, 2015.(PDF)
  • [50] H. Zhang, L. Zhang, H. Shen, "A Blind Super-resolution Reconstruction Method Considering Image Registration Errors", International Journal of Fuzzy Systems, vol. 17, no. 2, pp. 353-364, 2015.(PDF)
  • [51] X. Li, H. Shen, L. Zhang, H. Zhang, Q. Yuan, and G. Yang, "Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multi-temporal Dictionary Learning," IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 11, pp. 7086 - 7098, 2014.(PDF)
  • [52] J. Li, H. Zhang, L. Zhang, X. Huang, L. Zhang, "Joint Collaborative Representation with Multitask Learning for Hyperspectral Image Classification", IEEE Trans. on Geoscience and Remote Sensing, vol. 52, no. 9, pp. 5923-5936, 2014.(PDF)
  • [53] H. Zhang, W. He, L. Zhang, H. Shen, Q. Yuan, "Hyperspectral Image Restoration Using Low-Rank Matrix Recovery", IEEE Trans. on Geoscience and Remote Sensing, vol. 52, no. 8, pp. 4729-4743, 2014. (ESI Hot Paper, ESI Highly Cited Paper)(PDF)
  • [54] J. Li, H. Zhang, L. Zhang, "Column-Generation Kernel Nonlocal Joint Collaborative Representation for Hyperspectral Image Classification", ISPRS Journal of Photogrammetry and Remote Sensing, vol. 94, no. 8, pp. 25-36, 2014. (PDF)
  • [55] J. Li, H. Zhang, L. Zhang, "Supervised Segmentation of Very High Resolution Images by the Use of Extended Morphological Attribute Profiles and a Sparse Transform", IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 8, pp. 1409-1413, 2014. (PDF)
  • [56] J. Li, H. Zhang, Y. Huang, L. Zhang, "Hyperspectral Image Classification by Nonlocal Joint Collaborative Representation with a Locally Adaptive Dictionary", IEEE Trans. on Geoscience and Remote Sensing, vol. 52, no. 6, pp. 3707-3719, 2014. (ESI Highly Cited Paper) (PDF)
  • [57] T. Hu, H. Zhang*, H. Shen, L. Zhang, "Robust Registration by Rank Minimization for Multiangle Hyper/Multispectral Remotely Sensed Imagery", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2443 - 2457, 2014.(PDF)
  • [58] H. Zhang, J. Li , Y. Huang, L. Zhang, "A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 6, pp. 2056 - 2065, 2014. (ESI Highly Cited Paper) (PDF)
  • [59] X. Meng, H. Shen, H. Zhang, L. Zhang, H. Li, "Maximum a Posteriori Fusion Method Based on Gradient Consistency Constraint for Multispectral/Panchromatic Remote Sensing Images," Spectroscopy and Spectral Analysis, vol. 34, no. 6, pp. 1332-1337, 2014.(PDF)
  • [60] C. Jiang, H. Zhang*, H. Shen, L. Zhang, "Two-Step Sparse Coding for the Pan-Sharpening of Remote Sensing Images", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 5, pp. 1792 - 1805, 2014.(PDF)
  • [61] M. Guo, H. Zhang*, J. Li, L. Zhang, H. Shen, "An Online Coupled Dictionary Learning Approach for Remote Sensing Image Fusion", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 4, pp. 1284-1294, 2014.(PDF)
  • [62] X. Li, H. Shen, L. Zhang, H. Zhang, Q. Yuan, "Dead Pixel Completion of Aqua MODIS Band 6 using a Robust M-Estimator Multi-Regression", IEEE Geoscience and Remote Sensing Letters, vol. 11, no. 4, pp. 768-772, 2014.(PDF)
  • [63] H. Zhang, Z. Yang, L. Zhang, H. Shen, "Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences", Remote Sensing, vol. 6, no. 1, pp. 637-657, 2014.(PDF)
  • [64] H. Shen, W. Jiang, H. Zhang, L. Zhang, "A piece-wise approach to removing the nonlinear and irregular stripes in MODIS data", International Journal of Remote Sensing, vol. 35, no. 1, pp. 44-53, 2014.(PDF)
  • [65] X. Xu, Y. Zhong, L. Zhang, H. Zhang, "Sub-Pixel Mapping Based on a MAP Model with Multiple Shifted Hyperspectral Imagery", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 580-593, 2013.(PDF)
  • [66] H. Zhang, H. Shen, L. Zhang, "A Super-Resolution Reconstruction Algorithm for Hyperspectral Images", Signal Processing, vol. 92, no. 9, pp. 2082-2096, 2012. (2012 Top 25 Hottest Article) (PDF)
  • [67] L. Zhang, H. Shen , W. Gong, H. Zhang, "Adjustable Model-Based Fusion Method for Multispectral and Panchromatic Images", IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 42, no. 6, pp. 1693-1704, 2012.(PDF)
  • [68] C. Jiang, H. Zhang, H. Shen, L. Zhang, "A Practical Compressed Sensing based Pan-Sharpening Method", IEEE Geoscience and Remote Sensing Letters, vol. 9, no.4, pp. 629-633, 2012. (PDF)
  • [69] L. Zhang, H. Zhang, H. Shen, P. Li, "A Super-Resolution Reconstruction Algorithm for Surveillance Images", Signal Processing, vol. 90, no. 3, pp. 848-859, 2010. (PDF)
会议论文
  • [1] Z. Wang, H. Zhang, W. He, L. Zhang, "Phenology Alignment Network: A Novel Framework for Cross-Regional Time Series Crop Classification", IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2021), 19–25 June, 2021.
  • [2] Y. Xia, Q. Huang, H. Zhang, "A Multi-model Fusion of Convolution Neural Network and Random Forest for Detecting Settlements without Electricity", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2021), Brussels, Belgium, 11–16 July, 2021.
  • [3] Z. Li, F. Lu, H. Zhang, G. Yang, L. Zhang, "Change Cross-detection based on Multi-model Fusion and Label Iteration", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2021), Brussels, Belgium, 11–16 July, 2021.
  • [4] S. Huang, H. Zhang, A. Pižurica, "Sketched Sparse Subspace Clustering Method for Large-Scale Hyperspectral Images", International Conference on Image Processing (ICIP 2020), Taipei, Taiwan China, 22–25 September, 2020.
  • [5] Y. Song, H. Zhang, L. Zhang, "Remote Sensing Image Spatiotemporal Fusion via Generative Adversarial Network through One Prior Image Pair", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2020), Hawaii, USA, 28 July–2 August, 2020.
  • [6] Y. Xia, Y. Liao, H. Zhang, G. Yang, "Land Cover Mapping Based on Multi-branch Fusion of Object-based and Pixel-based Segmentation with Filtered Labels", IEEE International Geoscience and Remote666dde4dse Sensing Symposium (IGRASS 2020), Hawaii, USA, 28 July–2 August, 2020.
  • [7] Y. Liao, H. Zhang, G. Yang, L. Zhang, "Learning Discriminative Global and Local Features for Building Extraction from Aerial Images", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2020), Hawaii, USA, 28 July–2 August, 2020.
  • [8] H. Chen, M. Lin, H. Zhang, G. Yang, G. Xia, X. Zheng, L. Zhang, "Multi-Level Fusion of Multi-Receptive Fields Contextual Networks and Disparity Network for Pairwise Semantic Stereo", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2019), Yokohama, Japan, 28 July–2 August, 2019.
  • [9] S. Huang, H. Zhang, A. Pižurica, "Landmark-based Large-Scale Sparse Subspace Clustering Method for Hyperspectral Images", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2019), Yokohama, Japan, 28 July–2 August, 2019. (EI)
  • [10] Y. Xia, H. Zhang, L. Zhang and Z. Fan, "Cloud Removal of Optical Remote Sensing Imagery with Multitemporal SAR-optical data using MonitorGAN", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2019), Yokohama, Japan, 28 July–2 August, 2019. (EI)
  • [11] C. Zhang, H. Zhang, Y. Liu and L. Zhang, "A heuristic exploration of bridging phenology-based and machine learning-based methods for paddy rice mapping with Sentinel-2 images", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2019), Yokohama, Japan, 28 July–2 August, 2019. (EI)
  • [12] Z. Chai, H. Zhang, X. Xu and L. Zhang, "Garlic Mapping for Sentinel-2 Time-Series Data Using A Random Forest Classifier", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2019), Yokohama, Japan, 28 July–2 August, 2019. (EI)
  • [13] P. Dai, H. Zhang, L. Zhang, H. Shen, "A Remote Sensing Spatiotemporal Fusion Model via Deep Learning", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2018), Valencia, Spain, 23–27 July, 2018.
  • [14] H. Xu, H. Zhang, W. He, L. Zhang, "Superpixel based Dimension Reduction for Hyperspectral Imagery", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2018), Valencia, Spain, 23–27 July, 2018.
  • [15] S. Huang, H. Zhang, A. Pižurica, "Joint Sparsity based Sparse Subspace Clustering for Hyperspectral Images", International Conference on Image Processing (ICIP 2018), Athens, Greece, 7–10 October, 2018.
  • [16] J. Kang, H. Zhang, H. Yang, L. Zhang, "Support Vector Machine Classification of Crop Lands using Sentinel-2 Imagery", International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2018), Hangzhou, China, 6–9 August, 2018.
  • [17] C. Zhang, H. Zhang, J. Du, L. Zhang, "Automated paddy rice extent extract with time stacks of Sentinel data: A case study in Jianghan Plain, Hubei, China", International Conference on Agro-Geoinformatics (Agro-Geoinformatics 2018), Hangzhou, China, 6–9 August, 2018.
  • [18] H. Zhai, H. Zhang, L. Zhang, P. Li, "Integrated research on land cover changes and the consequent influence: A case study of the western part of Tiaoxi Basin", International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2018), Xi’an, China, 17–20 June, 2018.
  • [19] S. Huang, H. Zhang, A. Pižurica, "Robust Joint Sparsity Model for Hyperspectral Image Classification", International Conference on Image Processing (ICIP 2017), Beijing, China, 17–20 September, 2017.
  • [20] H. Zhai, H. Zhang, L. Zhang, P. Li, "Total Variation Based Collaborative Representation Model With an Adaptive Sub-Dictionary for Hyperspectral Remote Sensing Imagery Clustering", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2017), Fort Worth, USA, 23–27 July, 2017.
  • [21] R. Luo, W. Liao, H. Zhang, Y. Pi, W. Philips, "Spectral-Spatial Classification of Hyperspectral Images with Semi-Supervised Graph Learning", SPIE REMOTE SENSING 2016, Edinburgh, UK, 26-29 September, 2016.
  • [22] W. Liao, F. Van Coillie, H. Zhang, S. Gautama and W. Philips, "Fusion of Optical and LIDAR Images for Urban Objects Recognition", GEOBIA 2016, Enschede, Netherlands, 14-16 September, 2016.
  • [23] W. Liao, H. Zhang, J. Li, S. Huang, R. Wang, R. Luo, A. Pižurica, "Fusion of Spectral and Spatial Information for Land Cover Classification", IEICE Information and Communication Technology Forum 2016, Patras, Greece, 6-8 July, 2016.
  • [24] S. Huang, W. Liao, H. Zhang, A. Pižurica, "Paint Loss Detection in Old Paintings by Sparse Representation Classification", International Traveling Workshop on Interactions Between Sparse Models and Technology 2016, Aalborg, Denmark, 24-26 August, 2016.
  • [25] H. Zhang, W. He, W. Liao, R. Luo, L. Zhang, A. Pižurica, "Exploiting the Low-Rank Property of Hyperspectral Imagery: A Technical Overview", IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016), California, USA, 21-24 August, 2016.
  • [26] H. Li, H. Shen, Q. Yuan, H. Zhang, L. Zhang, L. Zhang, "Quality Improvement of Hyperspectral Remote Sensing Images: A Technical Overview", IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016), California, USA, 21-24 August, 2016.
  • [27] R. Wang, H. C. Li, W. Liao, H. Zhang, A. Pižurica, "Hyperpsectral Unmixing by Reweighted Low Rank Representation", IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016), California, USA, 21-24 August, 2016.
  • [28] H. Zhai, H. Zhang, L. Zhang, P. Li "Squaring Weighted Low-rank Subspace Clustering for Hyperspectral Image Band Selection", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2016), Beijing, China, 10–15 July, 2016.
  • [29] W. He, H. Zhang, L. Zhang, "Hyperspectral Unmixing Using Total Variation Regularized Reweighted Sparse Non-Negative Matrix Factorization", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2016), Beijing, China, 10–15 July, 2016.
  • [30] H. Chen, H. Zhang, L. Zhang, "Robust Superresolution of Multiangle-Multispectral Remote Sensing Images based on Rank Minimization", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2016), Beijing, China, 10–15 July, 2016.
  • [31] H. Zhang, H. Zhai, W. Liao, L. Cao, L. Zhang, A. Pižurica, "Hyperspectral Image Kernel Sparse Subspace Clustering with Spatial Max Pooling Operation", the 23th International Society for Photogrammetry and Remote Sensing Congress (ISPRS 2016), Prague, Czech, 12–19 July, 2016.
  • [32] R. Luo, W. Liao, H. Zhang, L. Zhang, Y. Pi, W. Philips, "Classification of Cloudy Hyperspectral Image and LIDAR Data based on Feature Fusion and Desicion Fusion", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2016), Beijing, China, 10–15 July, 2016.
  • [33] J. Li, H. Zhang, L. Zhang, "Efficient Superpixel-Oriented Multi-task Joint Sparse Representation Classification for Hyperspectral imagery", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2015), Milan, Italy, 26–31 July, 2015.
  • [34] H. Zhai, H. Zhang, L. Zhang, P. Li, X. Xu, "Spectral-Spatial Clustering of Hyperspectral Remote Sensing Image with Sparse Subspace Clustering Model", IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2015), Tokyo, Japan, 2-5 June, 2015.
  • [35] W. He, H. Zhang, L. Zhang, H. Shen, "A Noise-Adjusted Iterative Randomized Singular Value Decomposition Method for Hyperspectral Image Denoising", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2014), Quebec, Canada, 13–18 July, 2014. (2014 IEEE IGARSS Student Paper Contest Top 3)
  • [36] J. Li, H. Zhang, L. Zhang, "Background Joint Sparse Representation for Hyperspectral Image Subpixel Anomaly Detection", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2014), Quebec, Canada, 13–18 July, 2014.
  • [37] J. Li, H. Zhang, L. Zhang, "A Nonlinear Regression Classification Algorithm with Small Sample Set for Hyperspectral Image", IEEE International Geoscience and Remote Sensing Symposium (IGRASS 2013), Melbourne, Australia, 21–26 July, 2013.
  • [38] X. Xu, Y. Zhong, L. Zhang, H. Zhang, R. Feng, "A Unified Sub-pixel Mapping Model Intergrating Spectral Unmixing for Hyperspectral Imagery", the 5th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2013), Gainesville, Florida, USA, 2013.
  • [39] H. Zhang, "Hyperspectral image denoising with cubic total variation model", the 22th International Society for Photogrammetry and Remote Sensing Congress (ISPRS 2012), Melbourne, 25-31 August,2012.
  • [40] J. Li, H. Zhang, Y. Huang, L. Zhang, "Classification for Hyperspectral Imagery Based on Nonlocal Weighted Joint Sparsity Model", the 4th IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2012), Shanghai, China, 4-7 June, 2012.
  • [41] H. Zhang, L. Zhang, H. Shen, P. Li, "A MAP approach for joint image registration, blur identification and super-resolution", the 5th International Conference on Image and Graphics( ICIG 2009), Xian, China, pp. 97-102, 21-24 September, 2009.
中文论文
  • [1] 张亚坤，张洪艳，沈焕锋，张良培， "一种基于稀疏表达的遥感影像时空融合方法"，电子科技，vol. 30, no. 11, pp. 56-59，2017.
  • [2] 帅滔，张洪艳，"基于新型阴影指标的遥感影像阴影检测方法"，电子科技，vol. 29, no. 2, 2016.
  • [3] 帅滔，张洪艳，张良培，"面向对象的高分辨率遥感影像阴影探测方法"，光子学报，vol. 44, no. 12, 2015.
  • [4] 姜湾, 沈焕锋, 曾超, 张良培, 张洪艳, "Terra卫星MODIS传感器28波段影像的条带噪声去除方法," 武汉大学学报(信息科学版), vol. 39, no. 5, pp.526-530, 2014.
  • [5] 张洪艳，沈焕锋，张良培，李平湘，袁强强，"基于最大后验估计的图像盲超分辨率重建方法"，计算机应用，vol. 31, no. 5, pp. 1209-1213, 2011.
  • [6] 刘瑜，徐爱锋，张洪艳，"GIS数据应用体系框架研究"，测绘与空间地理信息，vol. 34, no. 2, pp. 157-160, 2011.
  • [7] 徐源璟，汪俏珏，沈焕锋，李平湘，张洪艳，"基于刃边法与正则化方法的遥感影像复原"，测绘信息与工程，vol. 35, no. 6, pp. 7-9, 2010.
  • [8] 张洪艳，沈焕锋，张良培，李平湘，"一种保边缘图像超分辨率重建方法"，中国图象图形学报，vol. 14, no. 11, pp. 2255-2261, 2009.
  • [9] 刘璐，张洪艳，张良培， "基于光谱加权低秩矩阵分解的高光谱影像去噪方法"，电子科技，vol. 32, no. 7, 2019.
  • [10] 杨泽宇, 张洪艳*, 明金, 冷伟, 刘海启, 游炯, "深度学习应用于高分辨率遥感影像冬油菜提取"，测绘通报，vol. 0, no. 9, pp. 110-113. DOI: 10.13474/j.cnki.11-2246. 2020.0294, 2020.
  • [[11] 杨光义, 李卓鸿, 黄和, 张洪艳*, "基于地形约束的多尺度DEM融合"，武汉大学学报（工学版），under review, 2020.
教学论文
  • [1] 张洪艳, "浅谈高校教师素养对课堂教学的影响", 高教学刊, no. 11, pp. 210-211, 2016.
  • [2] 刘婷婷, 张洪艳, "遥感专业“计算机图形学”教学改革探讨", 大学教育, no. 11, pp. 138-139, 2014.
学术专著
软件著作
  • [1] 黄昕, 张洪艳, 钟燕飞, 张良培, “新型面向对象影像分类与变化检测系统”, 软件登记号: 2012SR004625, 批准时间: 2012-01-20.
  • [2] 张良培, 罗旭东, 钟燕飞, 张洪艳, “高光谱影像成像光谱分析软件”, 软件登记号: 2015SR071825, 批准时间: 2015-08-13.
专利申请
  • [1] 李家艺, 张洪艳, 张良培, “基于联合稀疏表达的遥感影像多尺度面向对象分类方法”, 专利号: ZL 2013 1 0628634.7, 授权日: 2016.05.21.
  • [2] 沈焕锋, 李兴华, 张良培, 张洪艳, “利用多时相数据去除光学遥感影像大面积厚云的方法”, 专利号: ZL 2012 1 0551692.X, 授权日: 2015.06.10.
  • [3] 张洪艳, 张亚坤, 沈焕锋, 袁强强, 张良培, “基于非耦合映射关系的影像超分辨率重建方法及系统”, 专利号: ZL 2016 1 0231568.3, 授权日: 2018.10.26.
  • [4] 张洪艳, 杜红雨, 张良培, “一种基于Sentinel-2影像数据的早期自动化小麦制图方法”, 专利号: ZL 2019 1 0165983.7, 授权日: 2020.11.17.
  • [5] 张洪艳, 王超, 张良培, “一种基于Yolo V3的多源视频影像重点目标快速检测方法”, 申请号: 201910143170.8, 申请日: 2019.02.26.
  • [6] 张洪艳, 王超, 张良培, “一种基于深度学习的高分辨率遥感影像弱检测方法”, 申请号: 201910870991.1, 申请日: 2019.09.16.
  • [7] 张洪艳，蔡静宜，沈焕锋，张良培，“基于双低秩矩阵分解的GF-5遥感影像混合噪声去除方法”, 申请号: 202010075853.7, 申请日: 2020.01.22.
  • [8] 张洪艳，柴朝阳，张良培，“一种基于Sentinel-2遥感影像的大蒜种植信息自动化提取方法”, 申请号: 202010371772.1, 申请日: 2020.05.06.
  • [9] 李家艺, 张洪艳, 张良培, “基于联合稀疏表达的遥感影像多尺度面向对象分类方法”, 专利号: ZL201310628634.7, 授权日: 2016.05.21.
  • [10] 沈焕锋, 李兴华, 张良培, 张洪艳, “利用多时相数据去除光学遥感影像大面积厚云的方法”, 专利号: ZL 201210551692.X, 授权日: 2015.06.10.
  • [11] 张洪艳, 张亚坤, 沈焕锋, 袁强强, 张良培, “基于非耦合映射关系的影像超分辨率重建方法及系统”, 申请号: 20161023.1568.3, 申请日: 2016.04.14.