遥感技术研究所
极化SAR/高光谱图像分类
  • Li ZC, Li HC*, Gao G, Hua ZX, Zhang F , Hong W, “Unsupervised classification of polarimetric SAR images via SVGp0MM with extended variational inference”, ISPRS Journal of Photogrammetry and Remote Sensing , 2023. ( Top )
  • Hu W, Wang F, Yin Q*, Zhang F , “SGT: A Generalized Processing Model for 1-D Remote Sensing Signal Classification”, IEEE Geoscience and Remote Sensing Letters , 2022.
  • Cheng J, Xiang D, Yin Q, Zhang F*, “A Novel Crop Classification Method Based on the Tensor-GCN for Time-Series PolSAR Data” IEEE Transactions on Geoscience and Remote Sensing , 2022. ( Top )
  • Ni J, Carlos LM*, Hu Z, Zhang F , “Multitemporal SAR and Polarimetric SAR Optimization and Classification: Reinterpreting Temporal Coherence” IEEE Transactions on Geoscience and Remote Sensing , 2022. ( Top )
  • Deng S, Yin Q,* Zhang F , Yuan X, “A Ship Ghost Interference Removal Method Based on Gaofen-3 Polarimetric SAR Data”, IGARSS2022 , 2022.
  • 张帆 , 闫敏超, 倪军, 项德良, “高阶条件随机场引导的多分支极化 SAR 图像分类”, 中国图象图形学报, 2023.
  • Yin Q, Li J, Zhou Y, Xiang D, Zhang F, “Adaptive weighted learning for vegetation contribution in soil moisture inversion using PolSAR data”, International Journal of Remote Sensing , 2022.
  • Ni J, Xiang D, Lin Z, Carlos LM, Hu W, Zhang F* , “DNN-based PolSAR image classification on noisy labels” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2022.
  • Zhou Y, Chen P, Liu N, Yin Q, Zhang F , “Graph-Embedding Balanced Transfer Subspace Learning for Hyperspectral Cross-Scene Classification” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022.
  • Zhang F, Cao Z, Xiang D, Hu C, Ma F, Yin Q, Zhou Y, “ Pseudo quad-pol simulation from compact polarimetric SAR data via a complex-valued dual-branch convolutional neural network”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021.
  • Ni J, Zhang F* , Yin Q, et al. Random neighbor pixel-block-based deep recurrent learning for polarimetric SAR image classification[J]. Transactions on Geoscience and Remote Sensing , 2021, 59(9):7557-7569. ( Top )
  • Cheng J, Zhang F , Xiang D, et al. PolSAR Image Classification With Multiscale Superpixel-Based Graph Convolutional Network[J]. Transactions on Geoscience and Remote Sensing , 2021. ( Top )
  • Zhang F , Yan M, Hu C, et al. Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification[J]. IEEE Geoscience and Remote Sensing Letters , 2021.
  • Yin Q, Xu J, Xiang D, Zhou Y, Zhang F . Polarimetric Decomposition With an Urban Area Descriptor for Compact Polarimetric SAR Data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021.
  • Yin Q, Li J, Ma F, Xiang D, Zhang F . Dual-Channel Convolutional Neural Network for Bare Surface Soil Moisture Inversion Based on Polarimetric Scattering Models[J]. Remote Sensing , 2021, 13(22): 4503. ( Top )
  • Cheng J, Zhang F , Xiang D, et al. PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network[J]. Remote Sensing , 2021, 13(16): 3132. ( Top )
  • Ni J, Zhang F , Ma F, et al. Random Region Matting for the High-Resolution PolSAR Image Semantic Segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021, 14: 3040-3051.
  • Wang Y, Cheng J, Zhou Y, Zhang F , Yin Q. A Multichannel Fusion Convolutional Neural Network Based on Scattering Mechanism for PolSAR Image Classification[J]. IEEE Geoscience and Remote Sensing Letters , 2021.
  • Yin Q, Wu Y, Zhang F* , et al. GPU-based soil parameter parallel inversion for PolSAR data[J]. Remote Sensing , 2020, 12(3): 415. ( Top )
  • Li Z, Ni J, Zhang F* , et al. Multi-GPU implementation of nearest-regularized subspace classifier for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2020, 13: 3534-3544.
  • Li H, Wang W, Ye S, Deng Y, Zhang F , Du Q. A mixture generative adversarial network with category multi-classifier for hyperspectral image classification[J]. Remote Sensing Letters , 2020, 11(11): 983-992.
  • Yin Q, Cheng J, Zhang F , et al. Interpretable POLSAR image classification based on adaptive-dimension feature space decision tree[J]. IEEE Access , 2020, 8: 173826-173837.
  • Yin Q, Hong W, Zhang F* , Eric P. Optimal combination of polarimetric features for vegetation classification in PolSAR image[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2019, 12(10): 3919-3931.
  • Ni J, Zhang F* , Yin Q, et al. Robust weighting nearest regularized subspace classifier for PolSAR imagery[J]. IEEE Signal Processing Letters , 2019, 26(10): 1496-1500.
  • Zhang F , Ni J, Yin Q, et al. Nearest-regularized subspace classification for PolSAR imagery using polarimetric feature vector and spatial information[J]. Remote Sensing , 2017, 9(11): 1114. ( Top )
  • Pan L, Li H C, Deng Y J, Zhang F , et al. Hyperspectral dimensionality reduction by tensor sparse and low-rank graph-based discriminant analysis[J]. Remote Sensing , 2017, 9(5): 452. ( Top )
  • Li W, Wu G, Zhang F , et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing , 2016, 55(2): 844-853. ( Top )
  • Li W, Du Q, Zhang F , et al. Hyperspectral image classification by fusing collaborative and sparse representations[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 9(9): 4178-4187.
  • Hu W, Huang Y, Wei L, Zhang F , et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors , 2015, 2015.
  • Li W, Du Q, Zhang F , et al. Collaborative-representation-based nearest neighbor classifier for hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters , 2014, 12(2): 389-393.
  • Xiong M, Zhang F , Ran Q, et al. Representation-based classifications with Markov random field model for hyperspectral urban data[J]. Journal of Applied Remote Sensing , 2014, 8(1): 085097.
  • Li Y, Yin Q, Wang Y, Lin Y, Zhang F , Hong W. Multi-aspect Polarimetric SAR Image Scattering Feature Information Coding and Classification with Machine Learning Approach[C]//EUSAR 2021; 13th European Conference on Synthetic Aperture Radar. VDE, 2021: 1-4.
  • Ni J, Jia Y, Yin Q, Zhou Y, Zhang F . Metric Learning Based Fine-Grained Classification for PolSAR Imagery[C]//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020: 716-719.
  • Wu Y, Yin Q*, Zhang F . GPU-Based Soil Parameter Parallel Inversion for PolSAR Imagery[C]//2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2019: 1-5. ( Excellent Paper Award Second Prize )
  • Zhang S, Yin Q, Ni J, Zhang F . PolSAR image classification with small sample learning based on CNN and CRF[C]//2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2019: 1-5.
  • Yin Q, Hong W, Zhang F , et al. Analysis of polarimetric feature combination based on PolSAR image classification performance with machine learning approach[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018: 8124-8127. ( Invited Talk )
    • SAR/光学图像目标解译
  • Ma F, Sun X, Zhang F , Zhou Y, Li HC, “What Catch Your Attention in SAR images: Saliency Detection based on Soft-Superpixel Lacunarity Cue”, IEEE Transactions on Geoscience and Remote Sensing , 2022. ( Top )
  • Zhou Y, Yang K, Ma F*, Hu W, Zhang F , “Water-land Segmentation via Structure-Aware CNN-Transformer Network on Large-scale SAR data”, IEEE Sensors Journal , 2022.
  • Meng T, Zhang F , Ma F, “A Target-region-based SAR ATR Adversarial Deception Method”, 2022 7th International Conference on Signal and Image Processing , 2022.
  • Wang D, Zhang F , Ma F, Hu W, Tang Y, Zhou Y, “A Benchmark Sentinel-1 SAR Dataset for Airport Detection”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2022.
  • Zhang F , Meng T, Xiang D, Ma F, Sun X, Zhou Y, “Adversarial deception against SAR target recognition network”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2022.
  • 张帆, 陆圣涛, 项德良, 袁新哲, “An improved superpixel-based CFAR method for high-resolution SAR image ship target detection”, 雷达学报 , 2022.
  • Zhou Y, Zhang F, Ma F, Xiang D, Zhang F , “Small vessel detection based on adaptive dual-polarimetric feature fusion and sea–land segmentation in SAR images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2022.
  • Ma F, Xiang D, Yang K, Yin Q, Zhang F , “Weakly Supervised Deep Soft Clustering for Flood Identification in SAR Images”, IEEE Geoscience and Remote Sensing Letters , 2022.
  • Tang J, Xiang D, Zhang F , Ma F, Zhou Y, Li HC, “Incremental SAR automatic target recognition with error correction and high plasticity”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2022.
  • Ma F, Zhang F , Xiang D, Yin Q, Zhou Y, “Fast task-specific region merging for SAR image segmentation”, IEEE Transactions on Geoscience and Remote Sensing , 2022. ( Top )
  • Ma F, Zhang F* , Zhang W, et al. Fast SAR Image Segmentation With Deep Task-Specific Superpixel Sampling and Soft Graph Convolution[J]. IEEE Transactions on Geoscience and Remote Sensing , 2020. ( Top )
  • Xiang D, Zhang F , Zhang W, et al. Fast pixel-superpixel region merging for SAR image segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing , 2020. ( Top )
  • Zhang F , Liu Y, Zhou Y, et al. A lossless lightweight CNN design for SAR target recognition[J]. Remote Sensing Letters , 2020, 11(5): 485-494.
  • Liu Y, Zhou Y, Zhou Y, Ma L, Wang B, Zhang F . Accelerating SAR Image Registration Using Swarm-Intelligent GPU Parallelization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2020, 13: 5694-5703.
  • Zhang F , Fu Z, Zhou Y, et al. Multi-aspect SAR target recognition based on space-fixed and space-varying scattering feature joint learning[J]. Remote Sensing Letters , 2019, 10(10): 998-1007.
  • Liu C, Li HC, Fu K, Zhang F , Datcu M, Emery WJ. Bayesian estimation of generalized gamma mixture model based on variational em algorithm[J]. Pattern Recognition , 2019, 87: 269-284. ( Top )
  • Yue K, Yang L, Li R, Hu W, Zhang F , Li W. Treeunet: Adaptive tree cnns for subdecimeter aerial image segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2019, 156:1-13. ( Top )
  • Huang L, Li W, Chen C, Zhang F , et al. Multiple features learning for ship classification in optical imagery[J]. Multimedia Tools and Applications , 2018, 77(11): 13363-13389.
  • Shi Q, Li W, Zhang F , et al. Deep CNN with multi-scale rotation invariance features for ship classification[J]. IEEE Access , 2018, 6: 38656-38668.
  • Zhang F , Wang Y, Ni J, et al. SAR target small sample recognition based on CNN cascaded features and AdaBoost rotation forest[J]. IEEE Geoscience and Remote Sensing Letters , 2019, 17(6): 1008-1012.
  • Li R, Liu W, Yang L, Sun S, Hu W, Zhang F , et al. DeepUNet: a deep fully convolutional network for pixel-level sea-land segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(11): 3954-3962.
  • Chen H, Zhang F* , Tang B, et al. Slim and efficient neural network design for resource-constrained SAR target recognition[J]. Remote Sensing , 2018, 10(10): 1618. ( Top )
  • Zhang F , Hu C, Yin Q, et al. Multi-aspect-aware bidirectional LSTM networks for synthetic aperture radar target recognition[J]. IEEE Access , 2017, 5: 26880-26891.
  • 金啸宇, 尹嫱, 倪军, 周勇胜, 张帆, 洪文. 一种基于场景合成和锚点约束的SAR目标检测网络, 南京信息工程大学学报, 2020, 12(2):210-215.
  • 王 璐, 张 帆*, 李 伟, 谢晓明, 胡 伟. 基于Gabor滤波器和局部纹理特征提取的SAR目标识别算法, 雷达学报, 2015, 4(6), 658-665. ( 《雷达学报》高被引论文 )
  • Tang J, Zhang F , Ma F, et al. How SAR Image Denoise Affects the Performance of DCNN-Based Target Recognition Method[C]//IGARSS 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.
  • Zhang F, Zhou Y, Zhang F , et al. Small Vessel Detection Based on Adaptive Dual-Polarimetric Sar Feature Fusion and Attention-Enhanced Feature Pyramid Network[C]//IGARSS 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.
  • Liu Y, Zhang F , Ma F, et al. Incremental Multitask SAR Target Recognition with Dominant Neuron Preservation[C]//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020: 754-757.
  • Du W, Zhang F , Ma F, et al. Improving SAR Target Recognition with Multi-Task Learning[C]//IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020: 284-287. ( Oral )
  • Huang H, Zhang F , Zhou Y, et al. High Resolution SAR image synthesis with hierarchical generative adversarial networks[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 2782-2785
  • Tang J, Zhang F , Zhou Y, et al. A fast inference networks for SAR target few-shot learning based on improved siamese networks[C]//IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2019: 1212-1215.
  • Fu Z, Zhang F , Yin Q, et al. Small sample learning optimization for ResNet based SAR target recognition[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018: 2330-2333.
    • SAR仿真成像
  • Zhang F , Zhao C, Han S, et al. GPU-Based Parallel Implementation of VLBI Correlator for Deep Space Exploration System[J]. Remote Sensing , 2021, 13(6): 1226. ( Top )
  • Zhang F , Yao X, Tang H, et al. Multiple mode SAR raw data simulation and parallel acceleration for Gaofen-3 mission[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2018, 11(6): 2115-2126.
  • Zhang F , Hu C, Li W, et al. Accelerating time-domain SAR raw data simulation for large areas using multi-GPUs[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2014, 7(9): 3956-3966.
  • Li Z, Su D, Zhu H, Li W, Zhang F* , et al. A fast synthetic aperture radar raw data simulation using cloud computing[J]. Sensors , 2017, 17(1): 113.
  • Zhang F , Hu C, Li W, et al. A deep collaborative computing based SAR raw data simulation on multiple CPU/GPU platform[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2016, 10(2): 387-399.
  • Zhang F , Li G, Li W, et al. Accelerating spaceborne SAR imaging using multiple CPU/GPU deep collaborative computing[J]. Sensors , 2016, 16(4): 494.
  • Zhang F , Hu C, Wu P C, et al. Accelerating aerial image simulation using improved CPU/GPU collaborative computing[J]. Computers & Electrical Engineering , 2015, 46: 176-189.
  • Zhang F , Li X, Liu H, et al. Three-dimensional terrain model multiview quality assessment considering human visual system[J]. Journal of Applied Remote Sensing , 2015, 9(1): 097290.
  • Zhang F , Li G, Li W, et al. Multiband microwave imaging analysis of ionosphere and troposphere refraction for spaceborne SAR[J]. International Journal of Antennas and Propagation , 2014, 2014.
  • Liang W, Jia Z, Qiu X, Hong J, Zhang Q, Lei B, Zhang F , et al. Polarimetric calibration of the GaoFen-3 mission using active radar calibrators and the applicable conditions of system model for radar polarimeters[J]. Remote Sensing , 2019, 11(2): 176. ( Top )
  • 胡辰, 张帆*, 李国君, 李伟, 崔忠马. 基于冗余计算约简的环扫SAR回波多GPU快速模拟[J]. 雷达学报, 2016, 5(4): 434-443.
  • 汪丙南, 张帆, 向茂生. 基于混合域的 SAR 回波快速算法[J]. 电子与信息学报, 2011, 33(3): 690-695.
  • 汪丙南, 张帆, 向茂生. 基线抖动对干涉 SAR 相位的影响[J]. 遥感学报, 2010 (6): 1171-1181.
  • 张帆，汪丙南，向茂生. 基于SAR回波仿真的BAQ压缩性能研究[J]. 系统仿真技术, 2010, 01.
  • 张帆, 洪文. 基于计算机图形学的 SAR 图像几何畸变仿真[J]. 系统仿真学报, 2009 (9): 2503-2508.
  • 张帆, 白璐, 洪文, 等. 基于计算机图形学的干涉 SAR 成像几何仿真[J]. 系统仿真学报, 2009 (8): 2195-2200.
  • 张帆, 林殷, 洪文. 基于网格计算的 SAR 回波分布式仿真[J]. 系统仿真学报, 2008, 20(12): 3165-3170.
  • Ma L, Zhu Y, Zhang F , et al. Spaceborne repeat-pass interferometric synthetic aperture radar experimental evaluation for the GaoFen-3 satellite[C]//IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018: 2168-2171.
  • Zhang F , Tang H, Yin Q, et al. Multiple mode SAR raw data simulation for GaoFen-3 mission evaluation[C]//2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017: 2097-2100.
  • Yao X, Zhang F , Sun X, et al. Comparison of distributed GPU computing frameworks for SAR raw data simulation[C]//2017 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2017: 5225-5228. (Oral)
  • Tang H, Li G, Zhang F , et al. A spaceborne SAR on-board processing simulator using mobile GPU[C]//2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016: 1198-1201.
  • Yao X, Hu C, Zhang F , et al. Atomic-free optimization on GPU based SAR raw data simulation[C]//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2016: 645-648.
  • Hu C, Zhang F , Ma L, et al. Efficient SAR raw data parallel simulation based on multicore vector extension[C]//2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015: 4719-4722.
  • Li G, Zhang F , Ma L, et al. Accelerating SAR imaging using vector extension on multi-core SIMD CPU[C]//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2015: 537-540.
  • Li G, Zhang F , Liu H, et al. Effect of ionosphere refraction on spaceborne SAR imaging precision[C]//2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. IEEE, 2013: 330-333.
  • Li H, Zhang F , Hu W. GPU rasterization based octree fast generation algorithm for terrain modeling[C]//2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS. IEEE, 2013: 282-285. ( Oral )
  • Zhang F , Wang B, Xiang M. Accelerating InSAR raw data simulation on GPU using CUDA[C]//2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010: 2932-2935.
  • Zhang F , Wang B, Xiang M. Accelerating InSAR raw data simulation on GPU using CUDA[C]//2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010: 2932-2935.
  • Wang B, Zhang F , Maosheng X. SAR raw signal simulation based on GPU parallel computation[C]//2009 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2009, 4: IV-617-IV-620.
  • Zhang F , Hong W, Li D. SAR image simulation of man-made scenes based on computer graphics[C]//IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008, 4: IV-1395-IV-1397.
  • Zhang F , Honw W. Analysis of squint angle in point target assessment[C]//2006 CIE International Conference on Radar. IEEE, 2006: 1-4.
  • Zhang F , Hu C, Yin Q, et al. A GPU based memory optimized parallel method for FFT implementation[J]. arXiv preprint arXiv:1707.07263, 2017.
    • 计算机视觉
  • Zhang R, Yang S, Zhang Q, Xu L, He Y, Zhang F . Graph-based few-shot learning with transformed feature propagation and optimal class allocation[J]. Neurocomputing , 2022, 470:247-256. ( Top )
  • Zhao Q, Hu W, Huang Y, Zhang F . P-DIFF+: Improving learning classifier with noisy labels by Noisy Negative Learning loss[J]. Neural Networks , 2021, 144:1-10.
  • Hu W, Huang Y, Zhang F* , et al. SeqFace: Learning discriminative features by using face sequences[J]. IET Image Processing , 2021, 15(11):2548-2558.
  • Zhu H, Zhuang Z, Zhou J, Zhang F , et al. Segmentation of liver cyst in ultrasound image based on adaptive threshold algorithm and particle swarm optimization[J]. Multimedia Tools and Applications , 2017, 76(6): 8951-8968.
  • Zhu H, Zhang F , Zhou J, et al. Estimation of fisheye camera external parameter based on second‐order cone programming[J]. IET Computer Vision , 2016, 10(5): 415-424.
  • Zhu H, Sheng J, Zhang F , et al. Improved maximally stable extremal regions based method for the segmentation of ultrasonic liver images[J]. Multimedia Tools and Applications , 2016, 75(18): 10979-10997.
  • Hu W, Huang Y, Zhang F, et al. Ray tracing via GPU rasterization[J]. The Visual Computer , 2014, 30(6): 697-706.
  • 张帆, 李晓阳, 刘欢, 等. 基于分形维数的 DEM 聚类简化方法研究[J]. 系统仿真学报, 2016, 28(2): 261-267.
  • 李晓阳, 祝海江, 胡伟, 等. 下视 SAR 数据 3 维表面重建[J]. 中国图象图形学报, 2016, 21(4): 456-463.
  • 李晓阳, 张帆, 祝海江, 等. 基于曲率熵和高斯混合模型的 DEM 简化算法研究[J]. 北京化工大学学报: 自然科学版, 2015 (6): 103-108.
  • Hu W, Zhao Q, Huang Y, Zhang F .P-DIFF: Learning Classifier with Noisy Labels based on Probability Difference Distributions[C]. International Conference on Pattern Recognition (ICPR), 2021, 144:1-10.
  • Hu W, Huang Y, Zhang F , et al. Noise-tolerant paradigm for training face recognition CNNs[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 11887-11896. ( CCF A )
  • Lin Y, Zhang F, Hu W. A novel 3D visualization method of SAR data[C], IET International Radar Conference, 2013.
  • Hu W, Li W, Zhang F, et al. Real-time Decolorization using Dominant Colors[J]. arXiv preprint arXiv:1404.2728, 2014.
    • 专著
  • Radar Systems: Technology, Principles and Applications (Chapter 6), Nova Science Publishers, 2013. (ISBN:978-1-62417-872-6)
    • 专利
  • 合成孔径雷达成像数据三维显示方法. ZL201310722014.X
  • 基于深度协同的合成孔径雷达回波并行模拟方法. ZL201610500585.2
  • 基于极化特征的自适应维度决策树分类方法. ZL201811489586.7