Yun-Song Li

Learned Hyperspectral Image Compression With Coarse-to-Fine Spatial–Spectral Prediction Network

While deep-learned hyperspectral image (HSI) compression has achieved remarkable progress, existing methods typically encode latent representations indiscriminately. This entangled paradigm fails to separate global structural priors from local spectral details, thereby bottlenecking the overall spectral fidelity. To ad...

Unified Model Compression Framework for Hyperspectral Image Super-Resolution

Hyperspectral image super-resolution (HSI-SR) is vital for fine-grained Earth observation but remains impractical on resource-constrained airborne and spaceborne platforms. While existing methods prioritize reconstruction fidelity and degradation robustness, they largely neglect hardware efficient. To address this gap,...

TMA-Fed: Text-Driven Modality-Aware Federated Learning for Multi-Satellite Remote Sensing

OpenGPL: Multimodal Open-Set Recognition via Dual-Level Pseudo-Unknown Generation and Positive–Negative Prompt Learning

The multimodal fusion of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has advanced remote sensing (RS) classification. However, existing methods are mainly based on the closed-set assumption and are thus less effective in real-world open-set scenarios where unknown categories may appear during...

UHFusion: Prompt-Guided Multi-Task Learning Network for Hyperspectral Image Fusion via Multi-Type Mixture-of-Experts

Hyperspectral image (HSI) fusion aims to generate high-resolution HSI by integrating low-resolution hyperspectral data with auxiliary high-resolution sources (e.g., panchromatic (PAN), RGB, or MSI). While recent deep learning-based HSI fusion approaches have achieved promising results, they are typically designed for s...

Planetary Scene Classification via a Novel Zero-Shot Hierarchical State-Space Model

Planetary scene classification plays a fundamental role in geomorphological analysis and autonomous exploration missions. However, planetary terrains exhibit high intraclass structural variability, and their analysis relies on an extremely limited set of annotated samples, making exhaustive premission labeling impracti...

Towards Efficient Embodied Reasoning: Mixture-of-Depth Compute Allocation for Vision-Language-Action Model

MoDeVLA is proposed, the first rate-distortion driven efficient VLA model that performs token-wise depth allocation via Mixture-of-Depth Conditioning and integrates shallow visual-spatial with deep textual-logical features for action conditioning and introduces Effective-Edge Flow, an action-aligned attribution measure...

Towards Efficient Embodied Reasoning: Mixture-of-Depth Compute Allocation for Vision-Language-Action Model

Vision-Language-Action (VLA) model plays a crucial role in embodied decision making. While practical deployment requires fast inference under limited onboard computation, a full forward pass through the vision-language model makes such deployment challenging. To address this issue, existing methods typically employ lig...