8 papers
Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery
Ti Ti Nguyen, Thanh-Dung Le, Vu Nguyen Ha +6
Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-us…
DT-Aided Resource Management in Spectrum Sharing Integrated Satellite-Terrestrial Networks
Hung Nguyen-Kha, Vu Nguyen Ha, Ti Ti Nguyen +3
The integrated satellite-terrestrial networks (ISTNs) through spectrum sharing have emerged as a promising solution to improve spectral efficiency and meet increasing wireless dema…
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT Models
Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen +9
Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and robust to sensor and transmission…
GLUSE: Enhanced Channel-Wise Adaptive Gated Linear Units SE for Onboard Satellite Earth Observation Image Classification
Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen +9
This study introduces ResNet-GLUSE, a lightweight ResNet variant enhanced with Gated Linear Unit-enhanced Squeeze-and-Excitation (GLUSE), an adaptive channel-wise attention mechani…
A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives
Ti Ti Nguyen, Thanh-Dung Le, Vu Nguyen Ha +9
The integration of machine learning (ML) has significantly enhanced the capabilities of Earth Observation (EO) systems by enabling the extraction of actionable insights from comple…
On-Air Deep Learning Integrated Semantic Inference Models for Enhanced Earth Observation Satellite Networks
Hong-fu Chou, Vu Nguyen Ha, Prabhu Thiruvasagam +7
Earth Observation (EO) systems are crucial for cartography, disaster surveillance, and resource administration. Nonetheless, they encounter considerable obstacles in the processing…