Selected Key Papers (3) :

1. SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification

Authors: [Jiaojiao Li]; Zhiyuan Zhang; Rui Song; Yunsong Li; Qian Du

Journal: IEEE Transactions on Image Processing

Topic: Spectral Reconstruction / Spectral Super-Resolution / HSI Generation

Year: 2024

Impact: The "SCFormer" introduces a novel framework for hyperspectral image classification, leveraging spectral coordinate priors to enhance model accuracy with minimal computational cost. It has inspired research on optimizing spectral band masks and lightweight network structures for better domain alignment in few-shot learning tasks. With 31 citations, the paper has contributed to advancing methods in cross-domain hyperspectral image classification.

2. Deep Hybrid 2-D–3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution

Authors: [Jiaojiao Li]; Chaoxiong Wu; Rui Song; Yunsong Li; Weiying Xie; Lihuo He

Journal: IEEE Transactions on Neural Networks and Learning Systems

Topic: Spectral Reconstruction / Spectral Super-Resolution / HSI Generation

Year: 2021

Impact: This paper improves spectral super-resolution by using dual second-order attention and camera spectral sensitivity prior. It has inspired research on better spatial-spectral feature correlation and enhanced feature representation. With 41 citations, it has advanced spectral super-resolution methods in remote sensing.

3. Adaptive Weighted Attention Network With Camera Spectral Sensitivity Prior for Spectral Reconstruction From RGB Images

Authors: [Jiaojiao Li]; Chaoxiong Wu; Rui Song; Yunsong Li; Fei Liu

Conference:  2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Topic: Spectral Reconstruction / Spectral Super-Resolution / HSI Generation

Year: 2020

Impact: This paper presents AWAN, a deep adaptive weighted attention network for spectral reconstruction from RGB images. It improves accuracy by integrating second-order non-local modules, channel attention, and a camera spectral sensitivity prior. With 167 citations, it has contributed significantly to spectral reconstruction techniques in remote sensing.

Google Scholar homepage: https://scholar.google.com/citations?hl=zh-CN&user=Ccu3-acAAAAJ&view_op=list_works&sortby=pubdate

Open Source

  https://github.com/jojolee6513/IPRSIG/blob/master/README.md

Hope for guidance and  exchanges!

1. B. Xi, Y. Zhang, J. Li, Y. Huang, Y. Li, Z. Li, "Transductive Few-Shot Learning With Enhanced Spectral-Spatial Embedding for Hyperspectral Image Classification," in IEEE Transactions on Image Processing, vol. 34, pp. 854-868, 2025, doi: 10.1109/TIP.2025.3531709.
2. J. Li D. Zhu, R. Song, H. Xu, Y. Li and Q. Du, "Multi-Feature Interaction and Degradation Estimation Transformer for Spectral Compressive Imaging," in IEEE Transactions on Circuits and Systems for Video Technology, doi: 10.1109/TCSVT.2025.3543569.
3. B. Xi, W. Zhang, J. Li, R. Song and Y. Li, "HyperTaFOR: Task-Adaptive Few-Shot Open-Set Recognition With Spatial-Spectral Selective Transformer for Hyperspectral Imagery," in IEEE Transactions on Image Processing, vol. 34, pp. 4148-4160, 2025, doi: 10.1109/TIP.2025.3555069.
4. Y. Leng, J. Li, R. Song, Y. Li and Q. Du, "Uncertainty-Guided Discriminative Priors Mining for Flexible Unsupervised Spectral Reconstruction," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2025.3526159.
5. J. Li, H. Li, H. Xu, R. Song, Y. Li and Q. Du, "Background Suppression Network With Attention Collapse Inhibited Transformer for Optical Remote Sensing Object Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 63, pp. 1-13, 2025, Art no. 5602413, doi: 10.1109/TGRS.2024.3520299.
6. J. Li, Z. Zhang, Y. Liu, R. Song, Y. Li and Q. Du, "SWFormer: Stochastic Windows Convolutional Transformer for Hybrid Modality Hyperspectral Classification," in IEEE Transactions on Image Processing, vol. 33, pp. 5482-5495, 2024, doi: 10.1109/TIP.2024.3465038.
7. J. Li, S. Duan, Y. Leng, R. Song, Y. Li and Q. Du, "Residual Mask in Cascaded Convolutional Transformer for Spectral Reconstruction," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024, Art no. 5523615, doi: 10.1109/TGRS.2024.3427633.
8. J. Li, Z. Zhang, R. Song, Y. Li and Q. Du, "SCFormer: Spectral Coordinate Transformer for Cross-Domain Few-Shot Hyperspectral Image Classification," in IEEE Transactions on Image Processing, vol. 33, pp. 840-855, 2024, doi: 10.1109/TIP.2024.3351443.
9. J. Li, P. Tian, R. Song, H. Xu, Y. Li and Q. Du, "PCViT: A Pyramid Convolutional Vision Transformer Detector for Object Detection in Remote-Sensing Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024, Art no. 5608115, doi: 10.1109/TGRS.2024.3360456.
10. J. Li, Y. Liu, R. Song, W. Liu, Y. Li and Q. Du, "HyperMLP: Superpixel Prior and Feature Aggregated Perceptron Networks for Hyperspectral and LiDAR Hybrid Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-14, 2024, Art no. 5505614, doi: 10.1109/TGRS.2024.3355037. 
11. K. Cao, J. Li, R. Song, Z. Liu and Y. Li, "Model-Driven Deep Pipeline With Uncertainty-Aware Bundle Adjustment for Satellite Photogrammetry," in IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-13, 2024, Art no. 5605313, doi: 10.1109/TGRS.2024.3352072.
12. J. Li, S. Du, R. Song, Y. Li and Q. Du, "Progressive Spatial Information-Guided Deep Aggregation Convolutional Network for Hyperspectral Spectral Super-Resolution," in IEEE Transactions on Neural Networks and Learning Systems, vol. 36, no. 1, pp. 1677-1691, Jan. 2025, doi: 10.1109/TNNLS.2023.3325682.
13. J. Li, Y. Leng, R. Song, W. Liu, Y. Li and Q. Du, "MFormer: Taming Masked Transformer for Unsupervised Spectral Reconstruction," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-12, 2023, Art no. 5508412, doi: 10.1109/TGRS.2023.3264976.
14. J. Li, Y. Diao, R. Song, B. Xi, Y. Li and Q. Du, "Class-Specific Autoaugment Architecture Based on Schmidt Mathematical Theory for Imbalanced Hyperspectral Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-15, 2023, Art no. 5525315, doi: 10.1109/TGRS.2023.3317885.
15. S. Duan, J. Li, R. Song, Y. Li, and Q. Du, "Unmixing-guided convolutional transformer for spectral reconstruction," Remote Sens., vol. 15, no. 10, p. 2619, 2023, doi: 10.3390/rs15102619.
16. K. Cao, J. Li, R. Song, and Y. Li, "HE²LM-AD: Hierarchical and efficient attitude determination framework with adaptive error compensation module based on ELM network," ISPRS J. Photogramm. Remote Sens., vol. 195, pp. 418-431, Jan. 2023, doi: 10.1016/j.isprsjprs.2022.12.010.
17. C. Wu, J. Li, R. Song, Y. Li and Q. Du, "HPRN: Holistic Prior-Embedded Relation Network for Spectral Super-Resolution," in IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 8, pp. 11409-11423, Aug. 2024, doi: 10.1109/TNNLS.2023.3260828.
18. C. Wu, J. Li, R. Song, Y. Li and Q. Du, "RepCPSI: Coordinate-Preserving Proximity Spectral Interaction Network With Reparameterization for Lightweight Spectral Super-Resolution," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-13, 2023, Art no. 5508313, doi: 10.1109/TGRS.2023.3264675.
19. K. Wang, F. Bai, J. Li, Y. Liu and Y. Li, "MashFormer: A Novel Multiscale Aware Hybrid Detector for Remote Sensing Object Detection," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 2753-2763, 2023, doi: 10.1109/JSTARS.2023.3254047.
20. S. Du, Y. Leng, X. Liang, J. Li, W. Liu and Q. Du, "Degradation Aware Unfolding Network for Spectral Super-Resolution," in IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 5501305, doi: 10.1109/LGRS.2023.3346929.
21. Z. Liu, J. Li, R. Song, C. Wu, W. Liu, Z. Li, and Y. Li, "Edge guided context aggregation network for semantic segmentation of remote sensing imagery," Remote Sens., vol. 14, no. 6, p. 1353, Mar. 2022, doi: 10.3390/rs14061353.
22. J. Li, S. Du, C. Wu, Y. Leng, R. Song, and Y. Li, "DRCR Net: Dense residual channel re-calibration network with non-local purification for spectral super resolution," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops, 2022, pp. 1259-1268.
23. J. Li, Y. Liu, R. Song, Y. Li, K. Han and Q. Du, "Sal²RN: A Spatial–Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-14, 2023, Art no. 5500114, doi: 10.1109/TGRS.2022.3231930.
24. J. Li, S. Du, R. Song, C. Wu, Y. Li and Q. Du, "HASIC-Net: Hybrid Attentional Convolutional Neural Network With Structure Information Consistency for Spectral Super-Resolution of RGB Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5522515, doi: 10.1109/TGRS.2022.3142258.
25. J. Li, Y. Ma, R. Song, B. Xi, D. Hong and Q. Du, "A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 5540513, doi: 10.1109/TGRS.2022.3213513.
26. J. Li, S. Zi, R. Song, Y. Li, Y. Hu and Q. Du, "A Stepwise Domain Adaptive Segmentation Network With Covariate Shift Alleviation for Remote Sensing Imagery," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5618515, doi: 10.1109/TGRS.2022.3152587.
27. J. Li et al., "Feature Guide Network With Context Aggregation Pyramid for Remote Sensing Image Segmentation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 9900-9912, 2022, doi: 10.1109/JSTARS.2022.3221860.
28. F. Hao, J. Li, R. Song, Y. Li, and K. Cao, "Mixed feature prediction on boundary learning for point cloud semantic segmentation," Remote Sens., vol. 14, no. 19, p. 4757, Sep. 2022, doi: 10.3390/rs14194757.
29. F. Hao, J. Li, R. Song, Y. Li and K. Cao, "Structure-Aware Graph Convolution Network for Point Cloud Parsing," in IEEE Transactions on Multimedia, vol. 25, pp. 7025-7036, 2023, doi: 10.1109/TMM.2022.3216951.
30. B. Xi, J. Li, Y. Li, R. Song, D. Hong and J. Chanussot, "Few-Shot Learning With Class-Covariance Metric for Hyperspectral Image Classification," in IEEE Transactions on Image Processing, vol. 31, pp. 5079-5092, 2022, doi: 10.1109/TIP.2022.3192712.
31. Y. Li, Y. Zheng, J. Li, R. Song and J. Chanussot, "Hyperspectral Pansharpening With Adaptive Feature Modulation-Based Detail Injection Network," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-17, 2022, Art no. 5538117, doi: 10.1109/TGRS.2022.3206880.
32. B. Xi et al., "DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 33, no. 4, pp. 1535-1548, April 2023, doi: 10.1109/TCSVT.2022.3215513.
33. F. Hao, R. Song, J. Li, K. Cao, and Y. Li, "Cascaded geometric feature modulation network for point cloud processing," Neurocomputing, vol. 492, pp. 474-487, Jul. 2022, doi: 10.1016/j.neucom.2022.04.007.
34. J. Li et al., "Deep Hybrid 2-D–3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution," in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 2, pp. 623-634, Feb. 2023, doi: 10.1109/TNNLS.2021.3098767.
35. J. Li, H. Zhang, R. Song, W. Xie, Y. Li and Q. Du, "Structure-Guided Feature Transform Hybrid Residual Network for Remote Sensing Object Detection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-13, 2022, Art no. 5610713, doi: 10.1109/TGRS.2021.3103964.
36. Y. Li, B. Xi, J. Li, R. Song, Y. Xiao and J. Chanussot, "SGML: A Symmetric Graph Metric Learning Framework for Efficient Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 609-622, 2022, doi: 10.1109/JSTARS.2021.3135548.
37. B. Xi, J. Li, Y. Li, R. Song, W. Sun and Q. Du, "Multiscale Context-Aware Ensemble Deep KELM for Efficient Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5114-5130, June 2021, doi: 10.1109/TGRS.2020.3022029.
38. B. Xi et al., "Multi-Direction Networks With Attentional Spectral Prior for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5500915, doi: 10.1109/TGRS.2020.3047682.
39. Y. Zheng et al., "Edge-Conditioned Feature Transform Network for Hyperspectral and Multispectral Image Fusion," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5513315, doi: 10.1109/TGRS.2021.3108122.
40. C. Wu, J. Li, R. Song and Y. Li, "Spectral Reconstruction Using Residual Channel Affinity Propagation Network with Structural Similarity Constraint," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 2476-2479, doi: 10.1109/IGARSS47720.2021.9554674.
41. B. Xi, J. Li, Y. Li and Q. Du, "Semi-Supervised Graph Prototypical Networks for Hyperspectral Image Classification," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 2851-2854, doi: 10.1109/IGARSS47720.2021.9553372.
42. Y. Zheng, J. Li, Y. Li, K. Cao and K. Wang, "Pansharpening of Hyperspectral Images with Detail Guided Feature Modulation," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4456-4459, doi: 10.1109/IGARSS47720.2021.9554728.
43. H. Zhang, J. Li, R. Song and Y. Li, "Multi-Scale Structure-Conditioned Feature Transform Network for Object Detection in Remote Sensing Imagery," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4208-4211, doi: 10.1109/IGARSS47720.2021.9553528.
44. K. Cao, J. Li, R. Song, Y. Li and W. Jiang, "An Extreme Learning Machine Correction Network for High Precision Satellite Attitude Determination," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 4612-4615, doi: 10.1109/IGARSS47720.2021.9555026.
45. B. Xi et al., "Semisupervised Cross-Scale Graph Prototypical Network for Hyperspectral Image Classification," in IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 11, pp. 9337-9351, Nov. 2023, doi: 10.1109/TNNLS.2022.3158280.
46. J. Li, C. Wu, R. Song, Y. Li, and W. Xie, "Residual augmented attentional U-shaped network for spectral reconstruction from RGB images," Remote Sens., vol. 13, no. 1, p. 115, Dec. 2021, doi: 10.3390/rs13010115.
47. J. Li, C. Wu, R. Song, Y. Li, and F. Liu, "Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images," in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops, 2020, pp. 462-463.
48. J. Li et al., "Hyperspectral Image Super-Resolution by Band Attention Through Adversarial Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 4304-4318, June 2020, doi: 10.1109/TGRS.2019.2962713.
49. J. Li et al., "Hybrid 2-D–3-D Deep Residual Attentional Network With Structure Tensor Constraints for Spectral Super-Resolution of RGB Images," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 3, pp. 2321-2335, March 2021, doi: 10.1109/TGRS.2020.3004934.
50. Y. Shi, J. Li, Y. Li and Q. Du, "Sensor-Independent Hyperspectral Target Detection With Semisupervised Domain Adaptive Few-Shot Learning," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 8, pp. 6894-6906, Aug. 2021, doi: 10.1109/TGRS.2020.3032528.
51. Y. Shi, J. Li, Y. Zheng, B. Xi and Y. Li, "Hyperspectral Target Detection With RoI Feature Transformation and Multiscale Spectral Attention," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 6, pp. 5071-5084, June 2021, doi: 10.1109/TGRS.2020.3001948.
52. Y. Zheng, J. Li, Y. Li, J. Guo, X. Wu and J. Chanussot, "Hyperspectral Pansharpening Using Deep Prior and Dual Attention Residual Network," in IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 11, pp. 8059-8076, Nov. 2020, doi: 10.1109/TGRS.2020.2986313.
53. W. Xie, Y. Cui, Y. Li, J. Lei, Q. Du and J. Li, "HPGAN: Hyperspectral Pansharpening Using 3-D Generative Adversarial Networks," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 1, pp. 463-477, Jan. 2021, doi: 10.1109/TGRS.2020.2994238.
54. B. Xi et al., "Deep Prototypical Networks With Hybrid Residual Attention for Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 13, pp. 3683-3700, 2020, doi: 10.1109/JSTARS.2020.3004973.
55. C. Wu, J. Li, R. Song and Y. Li, "Spectral Super-Resolution Using Hybrid 2D-3D Structure Tensor Attention Networks with Camera Spectral Sensitivity Prior," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 1857-1860, doi: 10.1109/IGARSS39084.2020.9323553.
56. Y. Shi, J. Li and Y. Li, "Hyperspectral Target Detection With RoI Feature Transformation," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 2193-2196, doi: 10.1109/IGARSS39084.2020.9323234.
57. Y. Zheng, J. Li, Y. Li, Y. Shi and J. Qu, "Deep Residual Spatial Attention Network for Hyperspectral Pansharpening," IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 2671-2674, doi: 10.1109/IGARSS39084.2020.9323620.
58. W. Xie, Y. Li, J. Lei, J. Yang, J. Li, X. Jia, and Z. Li, "Unsupervised spectral mapping and feature selection for hyperspectral anomaly detection," Neural Networks, vol. 132, pp. 144-154, Dec. 2020, doi: 10.1016/j.neunet.2020.08.010.
59. J. Li, R. Cui, B. Li, R. Song, Y. Li, and Q. Du, "Hyperspectral image super-resolution with 1D-2D attentional convolutional neural network," Remote Sens., vol. 11, no. 23, p. 2859, Dec. 2019, doi: 10.3390/rs11232859.
60. J. Li, Y. Li, R. Song, S. Mei and Q. Du, "Local Spectral Similarity Preserving Regularized Robust Sparse Hyperspectral Unmixing," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 10, pp. 7756-7769, Oct. 2019, doi: 10.1109/TGRS.2019.2916296.
61. J. Li, R. Cui, B. Li, Y. Li, S. Mei and Q. Du, "Dual 1D-2D Spatial-Spectral CNN for Hyperspectral Image Super-Resolution," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 3113-3116, doi: 10.1109/IGARSS.2019.8898352.
62. J. Li, R. Cui, Y. Li, B. Li, Q. Du and C. Ge, "Multitemporal Hyperspectral Image Super-Resolution through 3D Generative Adversarial Network," 2019 10th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp), Shanghai, China, 2019, pp. 1-4, doi: 10.1109/Multi-Temp.2019.8866956.
63. Y. Zheng, J. Li and Y. Li, "Hyperspectral Pansharpening Based on Guided Filter and Deep Residual Learning," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 616-619, doi: 10.1109/IGARSS.2019.8899015.
64. Y. Shi, J. Li, Y. Yin, B. Xi and Y. Li, "Hyperspectral Target Detection With Macro-Micro Feature Extracted by 3-D Residual Autoencoder," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 12, pp. 4907-4919, Dec. 2019, doi: 10.1109/JSTARS.2019.2939833.
65. S. Zhong et al., "Class Feature Weighted Hyperspectral Image Classification," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 12, pp. 4728-4745, Dec. 2019, doi: 10.1109/JSTARS.2019.2950876.
66. Y. Zheng, J. Li, Y. Li, K. Cao and K. Wang, "Deep Residual Learning for Boosting the Accuracy of Hyperspectral Pansharpening," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 8, pp. 1435-1439, Aug. 2020, doi: 10.1109/LGRS.2019.2945424.
67. J. Li, B. Xi, Y. Li, Q. Du, and K. Wang, "Hyperspectral classification based on texture feature enhancement and deep belief networks," Remote Sens., vol. 10, no. 3, p. 396, Mar. 2018, doi: 10.3390/rs10030396.
68. J. Li, B. Xi, Q. Du, R. Song, Y. Li, and G. Ren, "Deep kernel extreme-learning machine for the spectral–spatial classification of hyperspectral imagery," Remote Sens., vol. 10, no. 12, p. 2036, Dec. 2018, doi: 10.3390/rs10122036.
69. J. Li, Q. Du, Y. Li and W. Li, "Hyperspectral Image Classification With Imbalanced Data Based on Orthogonal Complement Subspace Projection," in IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 7, pp. 3838-3851, July 2018, doi: 10.1109/TGRS.2018.2813366.
70. J. Li, X. Zhao, Y. Li, Q. Du, B. Xi and J. Hu, "Classification of Hyperspectral Imagery Using a New Fully Convolutional Neural Network," in IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 2, pp. 292-296, Feb. 2018, doi: 10.1109/LGRS.2017.2786272.
71. J. Li, Q. Du, B. Xi and Y. Li, "Hyperspectral Image Classification Via Sample Expansion for Convolutional Neural Network," 2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, Netherlands, 2018, pp. 1-5, doi: 10.1109/WHISPERS.2018.8747245.
72. J. Li, B. Kingsdorf, and Q. Du, "Band selection for hyperspectral image classification using extreme learning machine," in Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 2017, p. 101980R, doi: 10.1117/12.2263039.
73. J. Li, Q. Du, and Y. Li, "An efficient radial basis function neural network for hyperspectral remote sensing image classification," Focus, vol. 20, pp. 4753-4759, 2016.
74. J. Li, Q. Du, and Y. Li, "Region-based collaborative sparse unmixing of hyperspectral imagery," in Proc. SPIE 9874, Remotely Sensed Data Compression, Communications, and Processing XII, 2016, p. 98740S, doi: 10.1117/12.2224489.
75. J. Li, Q. Du, W. Li and Y. Li, "Representation-based hyperspectral image classification with imbalanced data," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 3318-3321, doi: 10.1109/IGARSS.2016.7729858.
76. J. Li, W. Li, Q. Du, and Y. Li, "A generalized representation-based approach for hyperspectral image classification," in Proc. SPIE 9840, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXII, 2016, p. 98401, doi: 10.1117/12.2224494.
77. C. Ge, Y. Li, J. Li and K. Wang, "Subspace selection for hyperspectral pansharpening using sparse unmixing," 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China, 2016, pp. 7232-7235, doi: 10.1109/IGARSS.2016.7730886.
78. J. Li, Q. Du, W. Li, and Y. Li, "Improving the performance of extreme learning machine for hyperspectral image classification," in Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 2015, p. 950109, doi: 10.1117/12.2178013.
79. J. Li, Q. Du, W. Li, and Y. Li, "Optimizing extreme learning machine for hyperspectral image classification," J. Appl. Remote Sens., vol. 9, no. 1, p. 097296, Mar. 2015, doi: 10.1117/1.JRS.9.097296.
80. J. Li, Y. Li, X. Wu, J. Liu, K. Wang, and K. Liu, "Spatial correlations constrained sparse unmixing of hyperspectral image using adapting Markov random fields," in Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 2013, p. 88710E, doi: 10.1117/12.2022505.