10 papers
Radiative-Structured Neural Operator for Continuous Spectral Super-Resolution
Ziye Zhang, Bin Pan, Zhenwei Shi
Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in computer vision and remote sensing. Deep…
A Copula-Guided Temporal Dependency Method for Multitemporal Hyperspectral Images Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
Multitemporal hyperspectral unmixing (MTHU) aims to model variable endmembers and dynamical abundances, which emphasizes the critical temporal information. However, existing method…
SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing
Ruiying Li, Bin Pan, Qiaoying Qu +2
The performance of hyperspectral unmixing may be constrained by low spatial resolution, which can be enhanced using super-resolution in a multitask learning way. However, integrati…
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing
Ruiying Li, Bin Pan, Lan Ma +2
Multitemporal hyperspectral unmixing can capture dynamical evolution of materials. Despite its capability, current methods emphasize variability of endmembers while neglecting dyna…
CDG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theories
Zhe Gao, Bin Pan, Zhenwei Shi
Hyperspectral imagery (HSI) classification may suffer the challenge of hyperspectral-monospectra, where different classes present similar spectra. Joint spatial-spectral feature ex…
Domain Generalization Guided by Large-Scale Pre-Trained Priors
Zongbin Wang, Bin Pan, Shiyu Shen +2
Domain generalization (DG) aims to train a model from limited source domains, allowing it to generalize to unknown target domains. Typically, DG models only employ large-scale pre-…
Domain Agnostic Conditional Invariant Predictions for Domain Generalization
Zongbin Wang, Bin Pan, Zhenwei Shi
Domain generalization aims to develop a model that can perform well on unseen target domains by learning from multiple source domains. However, recent-proposed domain generalizatio…
Bayesian Domain Invariant Learning via Posterior Generalization of Parameter Distributions
Shiyu Shen, Bin Pan, Tianyang Shi +2
Domain invariant learning aims to learn models that extract invariant features over various training domains, resulting in better generalization to unseen target domains. Recently,…
Be Bayesian by Attachments to Catch More Uncertainty
Shiyu Shen, Bin Pan, Tianyang Shi +2
Bayesian Neural Networks (BNNs) have become one of the promising approaches for uncertainty estimation due to the solid theorical foundations. However, the performance of BNNs is a…
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery
Zhenjie Tang, Qing Xu, Zhenwei Shi +1
With the development of deep learning technology, multi-spectral image super-resolution methods based on convolutional neural network have recently achieved great progress. However…