6 papers
A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting
Yongchuan Cui, Peng Liu
Remote sensing imagery suffers from clouds, haze, noise, resolution limits, and sensor heterogeneity. Existing restoration and fusion approaches train separate models per degradati…
Mixture-of-Experts in Remote Sensing: A Survey
Yongchuan Cui, Peng Liu, Lajiao Chen
Remote sensing data analysis and interpretation present unique challenges due to the diversity in sensor modalities and spatiotemporal dynamics of Earth observation data. Mixture-o…
Leveraging Large-Scale Pretrained Spatial-Spectral Priors for General Zero-Shot Pansharpening
Yongchuan Cui, Peng Liu, Yi Zeng
Existing deep learning methods for remote sensing image fusion often suffer from poor generalization when applied to unseen datasets due to the limited availability of real trainin…
Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need
Yongchuan Cui, Peng Liu, Hui Zhang
Existing deep learning-based models for remote sensing pansharpening exhibit exceptional performance on training datasets. However, due to sensor-specific characteristics and varyi…
A Decade of Deep Learning for Remote Sensing Spatiotemporal Fusion: Advances, Challenges, and Opportunities
Enzhe Sun, Yongchuan Cui, Peng Liu +1
Remote sensing spatiotemporal fusion (STF) addresses the fundamental trade-off between temporal and spatial resolution by combining high temporal-low spatial and high spatial-low t…
Overcoming the Identity Mapping Problem in Self-Supervised Hyperspectral Anomaly Detection
Yongchuan Cui, Jinhe Zhang, Peng Liu +2
The surge of deep learning has catalyzed considerable progress in self-supervised Hyperspectral Anomaly Detection (HAD). The core premise for self-supervised HAD is that anomalous…