1. “A Transformer-Based CrossDomain Few-Shot Learning Method for Hyperspectral Target Detection”.
   IEEE Transactions on Geoscience and Remote Sensing, 2024 (Major Revision)
   BIB

   @article{Feng2024,
     author = {},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {A Transformer-Based CrossDomain Few-Shot Learning Method for Hyperspectral Target Detection},
     year = {2024 (Major Revision)},
     published = {0}
   }

2. “Discriminative Vision Transformer for Heterogeneous Cross-Domain Hyperspectral Image Classification”.
   IEEE Transactions on Geoscience and Remote Sensing, 2024 (Under Review)
   BIB

   @article{Ye2024UnderReview,
     author = {},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Discriminative Vision Transformer for Heterogeneous Cross-Domain Hyperspectral Image Classification},
     year = {2024 (Under Review)},
     published = {0}
   }

3. “Physics-guided Super-resolutionCompressed Encoding Spectral Imaging System”.
   Optical Express, 2024 (Under Review)
   BIB

   @article{Feng2025,
     author = {},
     journal = {Optical Express},
     title = {Physics-guided Super-resolutionCompressed Encoding Spectral Imaging System},
     year = {2024 (Under Review)},
     published = {0}
   }

4. “Hierarchical Multi-Granularity Ship Classification Using Hierarchical Constrastive Learning with Learnable Class Quires”.
   IEEE Transactions on Geoscience and Remote Sensing, 2024 (Major Revision)
   BIB

   @article{Chen2024MajorRevision,
     author = {},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Hierarchical Multi-Granularity Ship Classification Using Hierarchical Constrastive Learning with Learnable Class Quires},
     year = {2024 (Major Revision)},
     published = {0}
   }

2024

1. “Mask-Guided Local–Global Attentive Network for Change Detection in Remote Sensing Images”.
   F. Xiong, T. Li, J. Chen, J. Zhou, and Y. Qian.
   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 3366–3378, 2024
   DOI BIB

   @article{10381757,
     author = {Xiong, Fengchao and Li, Tianhan and Chen, Jingzhou and Zhou, Jun and Qian, Yuntao},
     journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
     title = {Mask-Guided Local–Global Attentive Network for Change Detection in Remote Sensing Images},
     year = {2024},
     pages = {3366-3378},
     volume = {17},
     doi = {10.1109/JSTARS.2024.3350044},
     keywords = {Feature extraction;Transformers;Remote sensing;Task analysis;Semantics;Memory management;Interference;Attention mechanism;change detection (CD);change mask;convolutional neural network (CNN);remote sensing image}
   }

2. “Adaptive Graph Modeling With Self-Training for Heterogeneous Cross-Scene Hyperspectral Image Classification”.
   M. Ye, J. Chen, F. Xiong, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–15, 2024
   DOI BIB

   @article{10379170,
     author = {Ye, Minchao and Chen, Junbin and Xiong, Fengchao and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Adaptive Graph Modeling With Self-Training for Heterogeneous Cross-Scene Hyperspectral Image Classification},
     year = {2024},
     pages = {1-15},
     volume = {62},
     doi = {10.1109/TGRS.2023.3348953},
     keywords = {Transfer learning;Adaptation models;Semantics;Correlation;Training;Sensors;Noise measurement;Adaptive graph modeling (AGM);cross-scene classification;heterogeneous transfer learning;hyperspectral image (HSI);self-training (ST)}
   }

3. “Hyperspectral Image Denoising via Spatial–Spectral Recurrent Transformer”.
   G. Fu, F. Xiong, J. Lu, J. Zhou, J. Zhou, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–14, 2024
   DOI BIB

   @article{10463066,
     author = {Fu, Guanyiman and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun and Zhou, Jiantao and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Hyperspectral Image Denoising via Spatial–Spectral Recurrent Transformer},
     year = {2024},
     pages = {1-14},
     volume = {62},
     doi = {10.1109/TGRS.2024.3374953},
     keywords = {Noise reduction;Correlation;Three-dimensional displays;Current transformers;Feature extraction;Convolution;Hyperspectral imaging;Deep learning;global spectral correlation (GSC);hyperspectral image (HSI) denoising;nonlocal spatial self-similarity (NSS);transformer}
   }

4. “Semantic-Aware Alignment Network for Cross-resolution Change Detection”.
   IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 2024, pp. 5455–5458
   DOI BIB

   @inproceedings{Zhang2024,
     author = {},
     booktitle = {IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Semantic-Aware Alignment Network for Cross-resolution Change Detection},
     year = {2024},
     pages = {5455-5458},
     doi = {10.1109/IGARSS52108.2023.10282593},
     keywords = {Wavelet transforms;Frequency-domain analysis;Semantics;Neural networks;Sensors;Remote sensing;Change detection;remote sensing image;Discrete Wavelet Transform;convolutional neural network}
   }

5. “Material-Guided Multiview Fusion Network for Hyperspectral Object Tracking”.
   Z. Li, F. Xiong, J. Zhou, J. Lu, Z. Zhao, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–15, 2024
   DOI BIB

   @article{10438474,
     author = {Li, Zhuanfeng and Xiong, Fengchao and Zhou, Jun and Lu, Jianfeng and Zhao, Zhuang and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Material-Guided Multiview Fusion Network for Hyperspectral Object Tracking},
     year = {2024},
     pages = {1-15},
     volume = {62},
     doi = {10.1109/TGRS.2024.3366536},
     keywords = {Feature extraction;Hyperspectral imaging;Target tracking;Videos;Object tracking;Visualization;Spatial resolution;Hyperspectral object tracking;hyperspectral unmixing;multihead attention;multiview fusion}
   }

6. “Iterative Low-rank Network for Hyperspectral Image Restoration”.
   J. Ye, F. Xiong, J. Zhou, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, 2024
   BIB

   @article{Ye2024,
     author = {Ye, Jin and Xiong, Fengchao and Zhou, Jun and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Iterative Low-rank Network for Hyperspectral Image Restoration},
     year = {2024}
   }

7. “SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising”.
   G. Fu, F. Xiong, J. Lu, and J. Zhou.
   IEEE Transactions on Geoscience and Remote Sensing, 2024
   BIB

   @article{Fu2024,
     author = {Fu, Guanyiman and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising},
     year = {2024}
   }

8. “Road Structure Based Ground-to-Aerial Pose Estimation”.
   D. Hu et al.
   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024
   BIB

   @article{Hu2024,
     author = {Hu, Di and Yuan, Xia and Xi, Huiying and Li, Jie and Song, Zhenbo and Xiong, Fengchao and Zhang, Kai and Zhao, Chunxia},
     journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
     title = {Road Structure Based Ground-to-Aerial Pose Estimation},
     year = {2024}
   }

9. “Wavelet Siamese Network with Semi-supervised Domain Adaptation for Remote Sensing Image Change Detection”.
   F. Xiong, T. Li, Y. Yang, J. Zhou, J. Lu, and Y. Qian.
   IEEE Trans. Geosci. Remote Sens., pp. 1–1, 2024
   DOI BIB

   @article{Xiong2024,
     author = {Xiong, Fengchao and Li, Tianhan and Yang, Yi and Zhou, Jun and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Trans. Geosci. Remote Sens.},
     title = {Wavelet Siamese Network with Semi-supervised Domain Adaptation for Remote Sensing Image Change Detection},
     year = {2024},
     pages = {1-1},
     doi = {10.1109/TGRS.2024.3432819},
     keywords = {Change detection;remote sensing image;frequency domain analysis;domain adaptation}
   }

2023

1. “An Attention-Based Multiscale Spectral–Spatial Network for Hyperspectral Target Detection”.
   S. Feng, R. Feng, J. Liu, C. Zhao, F. Xiong, and L. Zhang.
   IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023
   DOI BIB

   @article{Feng2023,
     author = {Feng, Shou and Feng, Rui and Liu, Jianfei and Zhao, Chunhui and Xiong, Fengchao and Zhang, Lifu},
     journal = {IEEE Geoscience and Remote Sensing Letters},
     title = {An Attention-Based Multiscale Spectral–Spatial Network for Hyperspectral Target Detection},
     year = {2023},
     pages = {1-5},
     volume = {20},
     doi = {10.1109/LGRS.2023.3265938},
     keywords = {Feature extraction;Convolutional neural networks;Testing;Detectors;Training;Transformers;Object detection;Hyperspectral images (HSIs);Siamese structure;target detection;vision Transformer (ViT)}
   }

2. “Multitask Sparse Representation Model-Inspired Network for Hyperspectral Image Denoising”.
   F. Xiong, J. Zhou, J. Zhou, J. Lu, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–15, 2023
   DOI BIB

   @article{10198268,
     author = {Xiong, Fengchao and Zhou, Jiantao and Zhou, Jun and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Multitask Sparse Representation Model-Inspired Network for Hyperspectral Image Denoising},
     year = {2023},
     pages = {1-15},
     volume = {61},
     doi = {10.1109/TGRS.2023.3300542},
     keywords = {Noise reduction;Correlation;Feature extraction;Optimization;Redundancy;Dictionaries;Computational modeling;Deep unfolding;hyperspectral image (HSI) denoising;multitask learning;sparse representation (SR)}
   }

3. “Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking”.
   Z. Li, F. Xiong, J. Zhou, J. Lu, and Y. Qian.
   IEEE Transactions on Image Processing, vol. 32, pp. 2901–2914, 2023
   DOI BIB

   @article{10128966,
     author = {Li, Zhuanfeng and Xiong, Fengchao and Zhou, Jun and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Transactions on Image Processing},
     title = {Learning a Deep Ensemble Network With Band Importance for Hyperspectral Object Tracking},
     year = {2023},
     pages = {2901-2914},
     volume = {32},
     doi = {10.1109/TIP.2023.3263109},
     keywords = {Hyperspectral imaging;Target tracking;Object tracking;Visualization;Feature extraction;Task analysis;Videos;Object tracking;hyperspectral videos;learning to optimize;ensemble learning}
   }

4. “Iterative Refinement Network for Hyperspectral Image Denoising”.
   F. Xiong, J. Zhou, Z. Zhao, and Y. Qian.
   2023 IEEE International Conference on Multimedia and Expo (ICME), 2023, pp. 2753–2758
   DOI BIB

   @inproceedings{10219765,
     author = {Xiong, Fengchao and Zhou, Jun and Zhao, Zhuang and Qian, Yuntao},
     booktitle = {2023 IEEE International Conference on Multimedia and Expo (ICME)},
     title = {Iterative Refinement Network for Hyperspectral Image Denoising},
     year = {2023},
     pages = {2753-2758},
     doi = {10.1109/ICME55011.2023.00468},
     keywords = {Knowledge engineering;Noise reduction;Estimation;Iterative methods;Noise measurement;Task analysis;Hyperspectral imaging;Hyperspectral image denoising;deep learning;iterative refinement}
   }

5. “Multi-Task Attentional U-Net for Hyperspectral Image Denoising”.
   F. Xiong, Z. Gu, W. Zheng, T. Li, and J. Zhou.
   IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, pp. 7336–7339
   DOI BIB

   @inproceedings{10283365,
     author = {Xiong, Fengchao and Gu, Zhongyi and Zheng, Wenbin and Li, Tianhan and Zhou, Jun},
     booktitle = {IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Multi-Task Attentional U-Net for Hyperspectral Image Denoising},
     year = {2023},
     pages = {7336-7339},
     doi = {10.1109/IGARSS52108.2023.10283365},
     keywords = {Correlation;Noise reduction;Geoscience and remote sensing;Multitasking;Calibration;Usability;Hyperspectral imaging}
   }

6. “Cross-Domain Heterogeneous Hyperspectral Image Classification Based on Meta-Learning with Task-Adaptive Loss Function”.
   Y. Jin, M. Ye, F. Xiong, and Y. Qian.
   2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2023, pp. 1–5
   DOI BIB

   @inproceedings{10430600,
     author = {Jin, Yuheng and Ye, Minchao and Xiong, Fengchao and Qian, Yuntao},
     booktitle = {2023 13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
     title = {Cross-Domain Heterogeneous Hyperspectral Image Classification Based on Meta-Learning with Task-Adaptive Loss Function},
     year = {2023},
     pages = {1-5},
     doi = {10.1109/WHISPERS61460.2023.10430600},
     keywords = {Metalearning;Adaptation models;Signal processing algorithms;Classification algorithms;Task analysis;Optimization;Hyperspectral imaging;Hyperspectral image;cross-domain classification;meta-learning}
   }

7. “Deep Parameterized Neural Networks for Hyperspectral Image Denoising”.
   F. Xiong, J. Zhou, J. Zhou, J. Lu, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–15, 2023
   DOI BIB

   @article{10258356,
     author = {Xiong, Fengchao and Zhou, Jun and Zhou, Jiantao and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Deep Parameterized Neural Networks for Hyperspectral Image Denoising},
     year = {2023},
     pages = {1-15},
     volume = {61},
     doi = {10.1109/TGRS.2023.3318001},
     keywords = {Noise reduction;Training;Noise measurement;Neural networks;Image reconstruction;Convolutional neural networks;Computational modeling;Convolutional neural networks;hyperspectral image (HSI) denoising;learning to optimize (L2O);sparse representation (SR)}
   }

8. “Wavelet Siamese Network for Change Detection in Remote Sensing Images”.
   T. Li, F. Xiong, W. Zheng, Z. Li, J. Zhou, and Y. Qian.
   IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023, pp. 5455–5458
   DOI BIB

   @inproceedings{10282593,
     author = {Li, Tianhan and Xiong, Fengchao and Zheng, Wenbin and Li, Zhuanfeng and Zhou, Jun and Qian, Yuntao},
     booktitle = {IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Wavelet Siamese Network for Change Detection in Remote Sensing Images},
     year = {2023},
     pages = {5455-5458},
     doi = {10.1109/IGARSS52108.2023.10282593},
     keywords = {Wavelet transforms;Frequency-domain analysis;Semantics;Neural networks;Sensors;Remote sensing;Change detection;remote sensing image;Discrete Wavelet Transform;convolutional neural network}
   }

9. “Domain-invariant attention network for transfer learning between cross-scene hyperspectral images”.
   M. Ye, C. Wang, Z. Meng, F. Xiong, and Y. Qian.
   IET Computer Vision, vol. 17, no. 7, pp. 739–749, 2023
   PDF ABS DOI BIB

   Abstract Small-sample-size problem is always a challenge for hyperspectral image (HSI) classification. Considering the co-occurrence of land-cover classes between similar scenes, transfer learning can be performed, and cross-scene classification is deemed a feasible approach proposed in recent years. In cross-scene classification, the source scene which possesses sufficient labelled samples is used for assisting the classification of the target scene that has a few labelled samples. In most situations, different HSI scenes are imaged by different sensors resulting in their various input feature dimensions (i.e. number of bands), hence heterogeneous transfer learning is desired. An end-to-end heterogeneous transfer learning algorithm namely domain-invariant attention network (DIAN) is proposed to solve the cross-scene classification problem. The DIAN mainly contains two modules. (1) A feature-alignment CNN (FACNN) is applied to extract features from source and target scenes, respectively, aiming at projecting the heterogeneous features from two scenes into a shared low-dimensional subspace. (2) A domain-invariant attention block is developed to gain cross-domain consistency with a specially designed class-specific domain-invariance loss, thus further eliminating the domain shift. The experiments on two different cross-scene HSI datasets show that the proposed DIAN achieves satisfying classification results.

   @article{https://doi.org/10.1049/cvi2.12238,
     author = {Ye, Minchao and Wang, Chenglong and Meng, Zhihao and Xiong, Fengchao and Qian, Yuntao},
     journal = {IET Computer Vision},
     title = {Domain-invariant attention network for transfer learning between cross-scene hyperspectral images},
     year = {2023},
     number = {7},
     pages = {739-749},
     volume = {17},
     doi = {https://doi.org/10.1049/cvi2.12238},
     eprint = {https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12238},
     keywords = {hyperspectral imaging, pattern classification},
     url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12238}
   }

10. “Guest Editorial: Spectral imaging powered computer vision”.
    J. Zhou, F. Xiong, L. Tong, N. Yokoya, and P. Ghamisi.
    IET Computer Vision, vol. 17, no. 7, pp. 723–725, 2023
    PDF DOI BIB

    @article{https://doi.org/10.1049/cvi2.12242,
      author = {Zhou, Jun and Xiong, Fengchao and Tong, Lei and Yokoya, Naoto and Ghamisi, Pedram},
      journal = {IET Computer Vision},
      title = {Guest Editorial: Spectral imaging powered computer vision},
      year = {2023},
      number = {7},
      pages = {723-725},
      volume = {17},
      doi = {https://doi.org/10.1049/cvi2.12242},
      eprint = {https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/cvi2.12242},
      url = {https://ietresearch.onlinelibrary.wiley.com/doi/abs/10.1049/cvi2.12242}
    }

2022

1. “Cross-Scene Hyperspectral Image Classification Based on Cycle-Consistent Adversarial Networks”.
   Z. Meng, M. Ye, F. Yao, F. Xiong, and Y. Qian.
   IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 1912–1915
   DOI BIB

   @inproceedings{9883513,
     author = {Meng, Zhihao and Ye, Minchao and Yao, Futian and Xiong, Fengchao and Qian, Yuntao},
     booktitle = {IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Cross-Scene Hyperspectral Image Classification Based on Cycle-Consistent Adversarial Networks},
     year = {2022},
     pages = {1912-1915},
     doi = {10.1109/IGARSS46834.2022.9883513},
     keywords = {Training;Transfer learning;Geoscience and remote sensing;Generative adversarial networks;Generators;Hyperspectral imaging;Image classification;Hyperspectral image classification;heterogeneous transfer learning;cycle-consistent adversarial networks}
   }

2. “Learning a Deep Structural Subspace Across Hyperspectral Scenes With Cross-Domain VAE”.
   M. Ye, J. Chen, F. Xiong, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–13, 2022
   DOI BIB

   @article{9680683,
     author = {Ye, Minchao and Chen, Junbin and Xiong, Fengchao and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Learning a Deep Structural Subspace Across Hyperspectral Scenes With Cross-Domain VAE},
     year = {2022},
     pages = {1-13},
     volume = {60},
     doi = {10.1109/TGRS.2022.3142941},
     keywords = {Transfer learning;Feature extraction;Training;Sensors;Manifolds;Decoding;Task analysis;Cross-domain variational autoencoder (CDVAE);cross-scene classification;heterogeneous transfer learning;hyperspectral image (HSI)}
   }

3. “Nonlocal Spatial–Spectral Neural Network for Hyperspectral Image Denoising”.
   G. Fu, F. Xiong, J. Lu, J. Zhou, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022
   DOI BIB

   @article{9930129,
     author = {Fu, Guanyiman and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Nonlocal Spatial–Spectral Neural Network for Hyperspectral Image Denoising},
     year = {2022},
     pages = {1-16},
     volume = {60},
     doi = {10.1109/TGRS.2022.3217097},
     keywords = {Correlation;Noise reduction;Three-dimensional displays;Tensors;Road transportation;Logic gates;Task analysis;Deep learning (DL);global spectral correlation (GSC);hyperspectral image (HSI) denoising;nonlocal self-similarity (NSS);spatial-spectral correlation (SSC)}
   }

4. “Material-Guided Siamese Fusion Network for Hyperspectral Object Tracking”.
   Z. Li, F. Xiong, J. Lu, J. Zhou, and Y. Qian.
   ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 2809–2813
   DOI BIB

   @inproceedings{9746089,
     author = {Li, Zhuanfeng and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
     booktitle = {ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     title = {Material-Guided Siamese Fusion Network for Hyperspectral Object Tracking},
     year = {2022},
     pages = {2809-2813},
     doi = {10.1109/ICASSP43922.2022.9746089},
     keywords = {Training;Target tracking;Image color analysis;Conferences;Signal processing;Feature extraction;Object tracking;Hyperspectral object tracking;feature fusion;material unmixing;appearance modelling}
   }

5. “SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising”.
   F. Xiong, J. Zhou, S. Tao, J. Lu, J. Zhou, and Y. Qian.
   IEEE Transactions on Image Processing, vol. 31, pp. 5469–5483, 2022
   DOI BIB

   @article{9855427,
     author = {Xiong, Fengchao and Zhou, Jun and Tao, Shuyin and Lu, Jianfeng and Zhou, Jiantao and Qian, Yuntao},
     journal = {IEEE Transactions on Image Processing},
     title = {SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising},
     year = {2022},
     pages = {5469-5483},
     volume = {31},
     doi = {10.1109/TIP.2022.3196826},
     keywords = {Noise reduction;Noise measurement;Correlation;Neural networks;Training;Tensors;Sensors;Hyperspectral image denoising;model-based neural network;low-rank representation;multidimensional sparse representation}
   }

6. “SNMF-Net: Learning a Deep Alternating Neural Network for Hyperspectral Unmixing”.
   F. Xiong, J. Zhou, S. Tao, J. Lu, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–16, 2022
   DOI BIB

   @article{9444347,
     author = {Xiong, Fengchao and Zhou, Jun and Tao, Shuyin and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {SNMF-Net: Learning a Deep Alternating Neural Network for Hyperspectral Unmixing},
     year = {2022},
     pages = {1-16},
     volume = {60},
     doi = {10.1109/TGRS.2021.3081177},
     keywords = {Optimization;Training;Sparse matrices;Matrix decomposition;Hyperspectral imaging;Mixture models;Knowledge engineering;Hyperspectral unmixing;model-based neural network;nonnegative matrix factorization (NMF);sparse representation}
   }

7. “MAC-Net: Model-Aided Nonlocal Neural Network for Hyperspectral Image Denoising”.
   F. Xiong, J. Zhou, Q. Zhao, J. Lu, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–14, 2022
   DOI BIB

   @article{9631264,
     author = {Xiong, Fengchao and Zhou, Jun and Zhao, Qinling and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {MAC-Net: Model-Aided Nonlocal Neural Network for Hyperspectral Image Denoising},
     year = {2022},
     pages = {1-14},
     volume = {60},
     doi = {10.1109/TGRS.2021.3131878},
     keywords = {Noise reduction;Mathematical models;Three-dimensional displays;Correlation;Neural networks;Deep learning;Data models;Hyperspectral image (HSI) denoising;low-rank representation;model-based neural network;nonlocal representation}
   }

8. “Spatial-Spectral Convolutional Sparse Neural Network for Hyperspectral Image Denoising”.
   F. Xiong, M. Ye, J. Zhou, and Y. Qian.
   IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 1225–1228
   DOI BIB

   @inproceedings{9883667,
     author = {Xiong, Fengchao and Ye, Minchao and Zhou, Jun and Qian, Yuntao},
     booktitle = {IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Spatial-Spectral Convolutional Sparse Neural Network for Hyperspectral Image Denoising},
     year = {2022},
     pages = {1225-1228},
     doi = {10.1109/IGARSS46834.2022.9883667},
     keywords = {Convolutional codes;Visualization;Solid modeling;Three-dimensional displays;Noise reduction;Neural networks;Nonlinear filters;Hyperspectral image denoising;model-based deep learning;convolutional sparse coding;deep un-folding}
   }

9. “Cross-Domain Attention Network for Hyperspectral Image Classification”.
   C. Wang, M. Ye, L. Lei, F. Xiong, and Y. Qian.
   IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 1564–1567
   DOI BIB

   @inproceedings{9884454,
     author = {Wang, Chenglong and Ye, Minchao and Lei, Ling and Xiong, Fengchao and Qian, Yuntao},
     booktitle = {IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Cross-Domain Attention Network for Hyperspectral Image Classification},
     year = {2022},
     pages = {1564-1567},
     doi = {10.1109/IGARSS46834.2022.9884454},
     keywords = {Training;Costs;Transfer learning;Geoscience and remote sensing;Feature extraction;Classification algorithms;Labeling;hyperspectral image;cross-scene classification;few-shot learning;transfer learning;cross-domain attention mechanism}
   }

10. “Multitask Sparse Neural Network for Hyperspectral Image Denoising”.
    F. Xiong, M. Ye, J. Zhou, J. Lu, and Y. Qian.
    ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 2799–2803
    DOI BIB

    @inproceedings{9747001,
      author = {Xiong, Fengchao and Ye, Minchao and Zhou, Jun and Lu, Jianfeng and Qian, Yuntao},
      booktitle = {ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      title = {Multitask Sparse Neural Network for Hyperspectral Image Denoising},
      year = {2022},
      pages = {2799-2803},
      doi = {10.1109/ICASSP43922.2022.9747001},
      keywords = {Deep learning;Training;Speech coding;Noise reduction;Neural networks;Signal processing;Task analysis;Hyperspectral image denoising;modelbased neural network;multitask learning;sparse coding}
    }

11. “Ques-to-Visual Guided Visual Question Answering”.
    X. Wu, J. Lu, Z. Li, and F. Xiong.
    2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 4193–4197
    DOI BIB

    @inproceedings{9897277,
      author = {Wu, Xiangyu and Lu, Jianfeng and Li, Zhuanfeng and Xiong, Fengchao},
      booktitle = {2022 IEEE International Conference on Image Processing (ICIP)},
      title = {Ques-to-Visual Guided Visual Question Answering},
      year = {2022},
      pages = {4193-4197},
      doi = {10.1109/ICIP46576.2022.9897277},
      keywords = {Location awareness;Visualization;Fuses;Image processing;Semantics;Benchmark testing;Question answering (information retrieval);multi-head attention;multi-modal;visual question answering;channel attention}
    }

12. “Model-Inspired Deep Neural Networks for Hyperspectral Unmixing”.
    Y. Qian, F. Xiong, M. Ye, and J. Zhou.
    in Advances in Hyperspectral Image Processing Techniques, John Wiley & Sons, Ltd, 2022, pp. 363–403
    PDF ABS DOI BIB

    Summary Model-based and learning-based methods are two typical classes for hyperspectral unmixing. Model-based methods are interpretable but rely on the definition of a physical model and iterative optimization. Learning-based methods have high learning ability, but their network architectures are generic with low physical interpretability. In this chapter, we bridge model-based methods and learning-based methods and introduce model-inspired learning methods. Benefiting from both approaches, model-inspired learning methods are more interpretable with higher learning ability, unmixing efficiency, and effectiveness.

    @inbook{doi:https://doi.org/10.1002/9781119687788.ch13,
      author = {Qian, Yuntao and Xiong, Fengchao and Ye, Minchao and Zhou, Jun},
      chapter = {13},
      pages = {363-403},
      publisher = {John Wiley & Sons, Ltd},
      title = {Model-Inspired Deep Neural Networks for Hyperspectral Unmixing},
      year = {2022},
      isbn = {9781119687788},
      booktitle = {Advances in Hyperspectral Image Processing Techniques},
      doi = {https://doi.org/10.1002/9781119687788.ch13},
      eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781119687788.ch13},
      keywords = {deep unfolding, hyperspectral unmixing, deep learning, spectral mixture model},
      url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781119687788.ch13}
    }

2021

1. “NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing”.
   F. Xiong, J. Zhou, M. Ye, J. Lu, and Y. Qian.
   ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, pp. 1865–1869
   DOI BIB

   @inproceedings{9414084,
     author = {Xiong, Fengchao and Zhou, Jun and Ye, Minchao and Lu, Jianfeng and Qian, Yuntao},
     booktitle = {ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     title = {NMF-SAE: An Interpretable Sparse Autoencoder for Hyperspectral Unmixing},
     year = {2021},
     pages = {1865-1869},
     doi = {10.1109/ICASSP39728.2021.9414084},
     keywords = {Training;Learning systems;Speech coding;Tools;Decoding;Sparse matrices;Speech processing;Hyperspectral unmixing;model-based neural network;autoencoder;sparse coding}
   }

2. “Spectral-Spatial-Temporal Attention Network for Hyperspectral Tracking”.
   Z. Li, X. Ye, F. Xiong, J. Lu, J. Zhou, and Y. Qian.
   2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2021, pp. 1–5
   DOI BIB

   @inproceedings{9484032,
     author = {Li, Zhuanfeng and Ye, Xinhai and Xiong, Fengchao and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
     booktitle = {2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
     title = {Spectral-Spatial-Temporal Attention Network for Hyperspectral Tracking},
     year = {2021},
     pages = {1-5},
     doi = {10.1109/WHISPERS52202.2021.9484032},
     keywords = {Training;Deconvolution;Image color analysis;Convolution;Neural networks;Signal processing algorithms;Feature extraction;deep learning;hyperspectral tracking;spectral-spatial-temporal attention}
   }

3. “Cross-Scene Hyperspectral Feature Selection via Hybrid Whale Optimization Algorithm With Simulated Annealing”.
   J. Wang, M. Ye, F. Xiong, and Y. Qian.
   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 2473–2483, 2021
   DOI BIB

   @article{9345344,
     author = {Wang, Jianxi and Ye, Minchao and Xiong, Fengchao and Qian, Yuntao},
     journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
     title = {Cross-Scene Hyperspectral Feature Selection via Hybrid Whale Optimization Algorithm With Simulated Annealing},
     year = {2021},
     pages = {2473-2483},
     volume = {14},
     doi = {10.1109/JSTARS.2021.3056593},
     keywords = {Feature extraction;Optimization;Spirals;Space exploration;Search problems;Whales;Training;Cross-domain WOASA;cross-scene feature selection;hyperspectral images}
   }

4. “Learning a Model-Based Deep Hyperspectral Denoiser from a Single Noisy Hyperspectral Image”.
   G. Fu, F. Xiong, S. Tao, J. Lu, J. Zhou, and Y. Qian.
   2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 4131–4134
   DOI BIB

   @inproceedings{9553257,
     author = {Fu, Guanyiman and Xiong, Fengchao and Tao, Shuyin and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
     booktitle = {2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS},
     title = {Learning a Model-Based Deep Hyperspectral Denoiser from a Single Noisy Hyperspectral Image},
     year = {2021},
     pages = {4131-4134},
     doi = {10.1109/IGARSS47720.2021.9553257},
     keywords = {Training;Image coding;Noise reduction;Geoscience and remote sensing;Encoding;Numerical models;Noise measurement;Hyperspectral image denoising;model-based deep learning;sparse coding}
   }

5. “AF-Net: All-scale Feature Fusion Network for Road Extraction from Remote Sensing Images”.
   S. Zou, F. Xiong, H. Luo, J. Lu, and Y. Qian.
   2021 Digital Image Computing: Techniques and Applications (DICTA), 2021, pp. 1–8
   DOI BIB

   @inproceedings{9647235,
     author = {Zou, Shide and Xiong, Fengchao and Luo, Haonan and Lu, Jianfeng and Qian, Yuntao},
     booktitle = {2021 Digital Image Computing: Techniques and Applications (DICTA)},
     title = {AF-Net: All-scale Feature Fusion Network for Road Extraction from Remote Sensing Images},
     year = {2021},
     pages = {1-8},
     doi = {10.1109/DICTA52665.2021.9647235},
     keywords = {Image segmentation;Roads;Digital images;Semantics;Benchmark testing;Feature extraction;Sensors;road extraction;remote sensing images;multiscale feature representation;deep learning;attention}
   }

2020

1. “Material Based Object Tracking in Hyperspectral Videos”.
   F. Xiong, J. Zhou, and Y. Qian.
   IEEE Transactions on Image Processing, vol. 29, pp. 3719–3733, 2020
   DOI BIB

   @article{Xiong2020,
     author = {Xiong, Fengchao and Zhou, Jun and Qian, Yuntao},
     journal = {IEEE Transactions on Image Processing},
     title = {Material Based Object Tracking in Hyperspectral Videos},
     year = {2020},
     pages = {3719-3733},
     volume = {29},
     doi = {10.1109/TIP.2020.2965302},
     keywords = {Videos;Hyperspectral imaging;Image color analysis;Object tracking;Feature extraction;Correlation;Object tracking;hyperspectral imaging;material unmixing}
   }

2. “M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images”.
   X. Ye, F. Xiong, J. Lu, H. Zhao, and J. Zhou.
   2020 Digital Image Computing: Techniques and Applications (DICTA), 2020, pp. 1–8
   DOI BIB

   @inproceedings{9363420,
     author = {Ye, Xinhai and Xiong, Fengchao and Lu, Jianfeng and Zhao, Haifeng and Zhou, Jun},
     booktitle = {2020 Digital Image Computing: Techniques and Applications (DICTA)},
     title = {M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images},
     year = {2020},
     pages = {1-8},
     doi = {10.1109/DICTA51227.2020.9363420},
     keywords = {Semantics;Object detection;Detectors;Feature extraction;Optical imaging;Task analysis;Remote sensing;Convolutional neural network (CNN);object detection;feature fusion;remote sensing image;multi-scale analysis}
   }

3. “Nonconvex Nonseparable Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing”.
   F. Xiong, J. Zhou, J. Lu, and Y. Qian.
   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 6088–6100, 2020
   DOI BIB

   @article{9210778,
     author = {Xiong, Fengchao and Zhou, Jun and Lu, Jianfeng and Qian, Yuntao},
     journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
     title = {Nonconvex Nonseparable Sparse Nonnegative Matrix Factorization for Hyperspectral Unmixing},
     year = {2020},
     pages = {6088-6100},
     volume = {13},
     doi = {10.1109/JSTARS.2020.3028104},
     keywords = {Sparse matrices;Matrix decomposition;Hyperspectral imaging;Linear programming;Estimation;Computational modeling;Generalized minimax concave (GMC) regularization;hyperspectral unmixing;nonnegative matrix factorization (NMF);sparse representation}
   }

4. “Spectral Mixture Model Inspired Network Architectures for Hyperspectral Unmixing”.
   Y. Qian, F. Xiong, Q. Qian, and J. Zhou.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 10, pp. 7418–7434, 2020
   DOI BIB

   @article{9055228,
     author = {Qian, Yuntao and Xiong, Fengchao and Qian, Qipeng and Zhou, Jun},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Spectral Mixture Model Inspired Network Architectures for Hyperspectral Unmixing},
     year = {2020},
     number = {10},
     pages = {7418-7434},
     volume = {58},
     doi = {10.1109/TGRS.2020.2982490},
     keywords = {Artificial neural networks;Estimation;Mixture models;Network architecture;Optimization;Iterative algorithms;Training;Deep unfolding;iterative methods;model-inspired network;spectral unmixing}
   }

5. “BAE-Net: A Band Attention Aware Ensemble Network for Hyperspectral Object Tracking”.
   Z. Li, F. Xiong, J. Zhou, J. Wang, J. Lu, and Y. Qian.
   2020 IEEE International Conference on Image Processing (ICIP), 2020, pp. 2106–2110
   DOI BIB

   @inproceedings{9191105,
     author = {Li, Zhuanfeng and Xiong, Fengchao and Zhou, Jun and Wang, Jing and Lu, Jianfeng and Qian, Yuntao},
     booktitle = {2020 IEEE International Conference on Image Processing (ICIP)},
     title = {BAE-Net: A Band Attention Aware Ensemble Network for Hyperspectral Object Tracking},
     year = {2020},
     pages = {2106-2110},
     doi = {10.1109/ICIP40778.2020.9191105},
     keywords = {Videos;Hyperspectral imaging;Target tracking;Image color analysis;Object tracking;Machine learning;Color;deep learning;hyperspectral tracking;band selection;ensemble learning}
   }

6. “Nonlocal Low-Rank Nonnegative Tensor Factorization for Hyperspectral Unmixing”.
   F. Xiong, K. Qian, J. Lu, J. Zhou, and Y. Qian.
   IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 2157–2160
   DOI BIB

   @inproceedings{9324663,
     author = {Xiong, Fengchao and Qian, Kun and Lu, Jianfeng and Zhou, Jun and Qian, Yuntao},
     booktitle = {IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Nonlocal Low-Rank Nonnegative Tensor Factorization for Hyperspectral Unmixing},
     year = {2020},
     pages = {2157-2160},
     doi = {10.1109/IGARSS39084.2020.9324663},
     keywords = {Tensors;Hyperspectral imaging;Mathematical model;Signal to noise ratio;Matrix decomposition;Sparse matrices;Matrix converters;Hyperspectral unmixing;nonlocal self-similarity;nonnegative tensor factorization;low-rank representation}
   }

2019

1. “Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization”.
   F. Xiong, Y. Qian, J. Zhou, and Y. Y. Tang.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 4, pp. 2341–2357, 2019
   DOI BIB

   @article{Xiong2019,
     author = {Xiong, Fengchao and Qian, Yuntao and Zhou, Jun and Tang, Yuan Yan},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization},
     year = {2019},
     number = {4},
     pages = {2341-2357},
     volume = {57},
     doi = {10.1109/TGRS.2018.2872888},
     keywords = {Tensile stress;TV;Hyperspectral imaging;Noise reduction;Algebra;Semantics;Hyperspectral unmixing;nonnegative tensor factorization (NTF);spectral–spatial information;total variation (TV)}
   }

2. “Dynamic Material-Aware Object Tracking in Hyperspectral Videos”.
   F. Xiong, J. Zhou, J. Chanussot, and Y. Qian.
   2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019, pp. 1–6
   DOI BIB

   @inproceedings{8921176,
     author = {Xiong, Fengchao and Zhou, Jun and Chanussot, Jocelyn and Qian, Yuntao},
     booktitle = {2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)},
     title = {Dynamic Material-Aware Object Tracking in Hyperspectral Videos},
     year = {2019},
     pages = {1-6},
     doi = {10.1109/WHISPERS.2019.8921176},
     keywords = {Hyperspectral imaging;Videos;Image color analysis;Object tracking;Feature extraction;Lighting;Hyperspectral unmixing;object tracking;spectral variability;spectral-spatial features}
   }

3. “Deep Unfolded Iterative Shrinkage-Thresholding Model for Hyperspectral Unmixing”.
   Q. Qian, F. Xiong, and J. Zhou.
   IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019, pp. 2151–2154
   DOI BIB

   @inproceedings{8898675,
     author = {Qian, Qipeng and Xiong, Fengchao and Zhou, Jun},
     booktitle = {IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Deep Unfolded Iterative Shrinkage-Thresholding Model for Hyperspectral Unmixing},
     year = {2019},
     pages = {2151-2154},
     doi = {10.1109/IGARSS.2019.8898675},
     keywords = {Training;Estimation;Iterative methods;Encoding;Optimization;Matching pursuit algorithms;Neural networks;Deep unfolding;spectral unmixing;sparse coding;iterative methods}
   }

4. “Hyperspectral Restoration via L_0 Gradient Regularized Low-Rank Tensor Factorization”.
   F. Xiong, J. Zhou, and Y. Qian.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 10410–10425, 2019
   DOI BIB

   @article{8824180,
     author = {Xiong, Fengchao and Zhou, Jun and Qian, Yuntao},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Hyperspectral Restoration via $L_0$ Gradient Regularized Low-Rank Tensor Factorization},
     year = {2019},
     number = {12},
     pages = {10410-10425},
     volume = {57},
     doi = {10.1109/TGRS.2019.2935150},
     keywords = {Noise reduction;Image restoration;Hyperspectral imaging;Matrix decomposition;Correlation;Sparse matrices;Hyperspectral restoration;L₀ gradient regularization;low-rank representation;spectral–spatial information}
   }

2018

1. “Hyperspectral Imagery Denoising via Reweighed Sparse Low-Rank Nonnegative Tensor Factorization”.
   F. Xiong, J. Zhou, and Y. Qian.
   2018 25th IEEE International Conference on Image Processing (ICIP), 2018, pp. 3219–3223
   DOI BIB

   @inproceedings{8451087,
     author = {Xiong, Fengchao and Zhou, Jun and Qian, Yuntao},
     booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
     title = {Hyperspectral Imagery Denoising via Reweighed Sparse Low-Rank Nonnegative Tensor Factorization},
     year = {2018},
     pages = {3219-3223},
     doi = {10.1109/ICIP.2018.8451087},
     keywords = {Noise reduction;Tensile stress;Sparse matrices;Image restoration;Hyperspectral imaging;Image coding;Spectral analysis;Hyperspectral imagery;nonnegative tensor factorization;sparse coding;low-rank representation}
   }

2. “Superpixel-Based Nonnegative Tensor Factorization for Hyperspectral Unmixing”.
   F. Xiong, J. Chen, J. Zhou, and Y. Qian.
   IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, pp. 6392–6395
   DOI BIB

   @inproceedings{8518642,
     author = {Xiong, Fengchao and Chen, Jingzhou and Zhou, Jun and Qian, Yuntao},
     booktitle = {IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium},
     title = {Superpixel-Based Nonnegative Tensor Factorization for Hyperspectral Unmixing},
     year = {2018},
     pages = {6392-6395},
     doi = {10.1109/IGARSS.2018.8518642},
     keywords = {Tensile stress;Hyperspectral imaging;Roads;Manifolds;Three-dimensional displays;Hyperspectral unmixing;joint spectral-spatial information;superpixel;nonnegative tensor factorization}
   }

2017

1. “Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery”.
   Y. Qian, F. Xiong, S. Zeng, J. Zhou, and Y. Y. Tang.
   IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 3, pp. 1776–1792, 2017
   DOI BIB

   @article{Qian2017,
     author = {Qian, Yuntao and Xiong, Fengchao and Zeng, Shan and Zhou, Jun and Tang, Yuan Yan},
     journal = {IEEE Transactions on Geoscience and Remote Sensing},
     title = {Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery},
     year = {2017},
     number = {3},
     pages = {1776-1792},
     volume = {55},
     doi = {10.1109/TGRS.2016.2633279},
     keywords = {Tensile stress;Matrix decomposition;Hyperspectral imaging;Mixture models;Feature extraction;Distance measurement;Hyperspectral imagery (HSI);spectral unmixing;spectral-spatial structure;tensor factorization}
   }