His research field is Machine learning and its application to Hyperspectral Remote Sensing Image Processing. The research he has conducted covers supervised/unsupervised/semi-supervised classification, small sample classification, target detection, super-resolution reconstruction, multimodal fusion, etc. His research interests also include Deep learning and Natural language processing.

Recent Representative Papers

[1] M. Hu, J. Ren*, M. Hu and Z. Li. Cross-Domain Aggregation Frequency Fusion Transformer for Lightweight Image Super-Resolution [J]. PATTERN RECOGNITION, 2025, 171.

[2] M. Shi, J. Ren*. Multi-branch feature transformation cross-domain few-shot learning for hyperspectral image classification [J]. PATTERN RECOGNITION, 2025, 160.

[3] Y. Peng, W. Wu, J. Ren*, X. Yu. Novel GCN Model Using Dense Connection and Attention Mechanism for Text Classification [J]. Neural Processing Letters, 2024, 56(2).

[4] M. Shi, J. Ren*. A lightweight denserelation network with attention for hyperspectral image few-shot classification [J]. Engineering Applications of Artificial Intelligence, 2023, 126(C).

[5] J. Wang, J. Ren*, Y. Peng and M. Shi. Spectral Segmentation Multi-scale Feature Extraction Residual Networks for Hyperspectral Image Classification[J]. Remote Sensing, 2023, 15(17).

[6] Y. Peng, J. Ren*, J. Wang and M. Shi. Spectral-Swin Transformer with Spatial Feature Extraction Enhancement for Hyperspectral Image Classification[J]. Remote Sensing, 2023, 15(10).

[7] J. Wang, W. Wu and J. Ren*. BERT-PG: a two-branch associative feature gated filtering network for aspect sentiment classification[J]. Journal of Intelligent Information Systems, 2023.

[8] J. Ren*, M. Shi, J. Chen, R. Wang and X. Wang. Hyperspectral image classification using multi-level features fusion capsule network with a dense structure[J]. Applied Intelligence, 2023, 53(11).

[9] X. Wang, J. Ren*, R. Wang, W. Wu and J. Chen. Spatial-spectral hyperspectral image classification based on primary and secondary capsule network[J]. J. Appl. Remote Sens, 2021, 15(3).

[10] J. Ren, W. Wu, G. Liu, Z. Chen and R. Wang. Bidirectional Gated Temporal Convolution with Attention for text classification[J]. Neurocomputing, 2021, 455: 265-273.