Improved principal component analysis based hyperspectral image compression method. - Baisen Liu; Ye Zhang; Wulin Zhang (IGARSS, 2013)

Testing proportionality of two large-dimensional covariance matrices. - Lin Xu; Baisen Liu; Shurong Zheng; Shaokun Bao (Comput. Stat. Data Anal., 2014)

A new test for the proportionality of two large-dimensional covariance matrices. - Baisen Liu; Lin Xu; Shurong Zheng; Guo-Liang Tian (J. Multivar. Anal., 2014)

Estimating functional linear mixed-effects regression models. - Baisen Liu; Liangliang Wang; Jiguo Cao (Comput. Stat. Data Anal., 2017)

Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes. - Baisen Liu; Liangliang Wang; Jiguo Cao (Monte Carlo Methods Appl., 2018)

Supervised functional principal component analysis. - Yunlong Nie; Liangliang Wang; Baisen Liu; Jiguo Cao (Stat. Comput., 2018)

Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions. - Baisen Liu; Liangliang Wang; Yunlong Nie; Jiguo Cao (Comput. Stat. Data Anal., 2019)

Unsupervised Multitemporal Domain Adaptation With Source Labels Learning. - Baisen Liu; Guoming Gao; Yanfeng Gu (IEEE Geosci. Remote. Sens. Lett., 2019)

Testing proportionality of two high-dimensional covariance matrices. - Guanghui Cheng; Baisen Liu; Guo-Liang Tian; Shurong Zheng (Comput. Stat. Data Anal., 2020)

Bayesian robust estimation of partially functional linear regression models using heavy-tailed distributions. - Guodong Shan; Yiheng Hou; Baisen Liu (Comput. Stat., 2020)

Multitemporal Intrinsic Image Decomposition With Temporal-Spatial Energy Constraints for Remote Sensing Image Analysis. - Guoming Gao; Baisen Liu; Xiangrong Zhang; Xudong Jin; Yanfeng Gu (IEEE Trans. Geosci. Remote. Sens., 2022)

Talent Flow Model Based on Graph Attention Network. - Baisen Liu; Zongting Jia; Lili Zhao; Weili Kong (ICBASE, 2023)

A Scalable Self-supervised Learner for Hyperspectral Image Classification. - Weili Kong; Lin Qi; Baisen Liu; Jiaming Pei (Tiny Papers @ ICLR, 2023)

Fusing 3D-CNN and lightweight Swin Transformer networks for HSI. - Baisen Liu; Yuanjia Liu; Wulin Zhang; Yiran Tian (Tiny Papers @ ICLR, 2023)

A Normalized Spatial-Spectral Supervoxel Segmentation Method for Multispectral Point Cloud Data. - Likun Chen; Yanfeng Gu; Xian Li 001; Xiangrong Zhang; Baisen Liu (IEEE Trans. Geosci. Remote. Sens., 2023)

A New Method for False Alarm Suppression in Heterogeneous Change Detection. - Cong Xu; Baisen Liu; Zishu He (Remote. Sens., 2023)

Spectral Swin Transformer Network for Hyperspectral Image Classification. - Baisen Liu; Yuanjia Liu; Wulin Zhang; Yiran Tian; Weili Kong (Remote. Sens., 2023)

A Shared Encoder for Multi-Source Hyperspectral Images. - Weili Kong; Baisen Liu; Xiaojun Bi 002; Jiaming Pei (Tiny Papers @ ICLR, 2024)

A Universal Representation Mechanism for Multisource Remote Sensing Image. - Weili Kong; Baisen Liu; Xiaojun Bi 002; Yihan He (IEEE Geosci. Remote. Sens. Lett., 2024)

Instructional Mask Autoencoder: A Scalable Learner for Hyperspectral Image Classification. - Weili Kong; Baisen Liu; Xiaojun Bi 002; Jiaming Pei; Zheng Chen 017 (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

Open-Set Cross-Domain Hyperspectral Image Classification Based on Manifold Mapping Alignment. - Xiangrong Zhang; Baisen Liu (IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2024)

HyperSL: A Spectral Foundation Model for Hyperspectral Image Interpretation. - Weili Kong; Baisen Liu; Xiaojun Bi 002; Changdong Yu; Xinyao Li; Yushi Chen (IEEE Trans. Geosci. Remote. Sens., 2025)