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2020 – today
- 2024
[j16]Weili Kong
, Baisen Liu, Xiaojun Bi, Yihan He:
A Universal Representation Mechanism for Multisource Remote Sensing Image. IEEE Geosci. Remote. Sens. Lett. 21: 1-5 (2024)- [j15]Weili Kong, Baisen Liu, Xiaojun Bi, Jiaming Pei, Zheng Chen:
Instructional Mask Autoencoder: A Scalable Learner for Hyperspectral Image Classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17: 1348-1362 (2024) - [j14]Xiangrong Zhang, Baisen Liu:
Open-Set Cross-Domain Hyperspectral Image Classification Based on Manifold Mapping Alignment. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 17: 6241-6252 (2024) - [c5]Weili Kong, Baisen Liu, Xiaojun Bi, Jiaming Pei:
A Shared Encoder for Multi-Source Hyperspectral Images. Tiny Papers @ ICLR 2024 - 2023
- [j13]Cong Xu, Baisen Liu, Zishu He:
A New Method for False Alarm Suppression in Heterogeneous Change Detection. Remote. Sens. 15(7): 1745 (2023) - [j12]Baisen Liu, Yuanjia Liu, Wulin Zhang, Yiran Tian, Weili Kong:
Spectral Swin Transformer Network for Hyperspectral Image Classification. Remote. Sens. 15(15): 3721 (2023) - [j11]Likun Chen, Yanfeng Gu, Xian Li, Xiangrong Zhang, Baisen Liu:
A Normalized Spatial-Spectral Supervoxel Segmentation Method for Multispectral Point Cloud Data. IEEE Trans. Geosci. Remote. Sens. 61: 1-11 (2023) - [c4]Baisen Liu, Zongting Jia, Lili Zhao, Weili Kong:
Talent Flow Model Based on Graph Attention Network. ICBASE 2023: 115-121 - [c3]Weili Kong, Lin Qi, Baisen Liu, Jiaming Pei:
A Scalable Self-supervised Learner for Hyperspectral Image Classification. Tiny Papers @ ICLR 2023 - [c2]Baisen Liu, Yuanjia Liu, Wulin Zhang, Yiran Tian:
Fusing 3D-CNN and lightweight Swin Transformer networks for HSI. Tiny Papers @ ICLR 2023 - 2022
- [j10]Guoming Gao, Baisen Liu, Xiangrong Zhang, Xudong Jin, Yanfeng Gu:
Multitemporal Intrinsic Image Decomposition With Temporal-Spatial Energy Constraints for Remote Sensing Image Analysis. IEEE Trans. Geosci. Remote. Sens. 60: 1-16 (2022) - 2020
- [j9]Guanghui Cheng, Baisen Liu, Guo-Liang Tian, Shurong Zheng:
Testing proportionality of two high-dimensional covariance matrices. Comput. Stat. Data Anal. 150: 106962 (2020) - [j8]Guodong Shan, Yiheng Hou, Baisen Liu:
Bayesian robust estimation of partially functional linear regression models using heavy-tailed distributions. Comput. Stat. 35(4): 2077-2092 (2020)
2010 – 2019
- 2019
- [j7]Baisen Liu, Liangliang Wang, Yunlong Nie, Jiguo Cao:
Bayesian inference of mixed-effects ordinary differential equations models using heavy-tailed distributions. Comput. Stat. Data Anal. 137: 233-246 (2019) - [j6]Baisen Liu, Guoming Gao, Yanfeng Gu:
Unsupervised Multitemporal Domain Adaptation With Source Labels Learning. IEEE Geosci. Remote. Sens. Lett. 16(9): 1477-1481 (2019) - 2018
- [j5]Baisen Liu, Liangliang Wang, Jiguo Cao:
Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes. Monte Carlo Methods Appl. 24(1): 117-127 (2018) - [j4]Yunlong Nie, Liangliang Wang, Baisen Liu, Jiguo Cao:
Supervised functional principal component analysis. Stat. Comput. 28(3): 713-723 (2018) - 2017
- [j3]Baisen Liu, Liangliang Wang, Jiguo Cao:
Estimating functional linear mixed-effects regression models. Comput. Stat. Data Anal. 106: 153-164 (2017) - 2014
- [j2]Lin Xu, Baisen Liu, Shurong Zheng, Shaokun Bao:
Testing proportionality of two large-dimensional covariance matrices. Comput. Stat. Data Anal. 78: 43-55 (2014) - [j1]Baisen Liu, Lin Xu, Shurong Zheng, Guo-Liang Tian:
A new test for the proportionality of two large-dimensional covariance matrices. J. Multivar. Anal. 131: 293-308 (2014) - 2013
- [c1]Baisen Liu, Ye Zhang, Wulin Zhang:
Improved principal component analysis based hyperspectral image compression method. IGARSS 2013: 1478-1480
Coauthor Index
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