7 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…
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…
Semantic Knowledge Distillation for Onboard Satellite Earth Observation Image Classification
Thanh-Dung Le, Vu Nguyen Ha, Ti Ti Nguyen +8
This study presents an innovative dynamic weighting knowledge distillation (KD) framework tailored for efficient Earth observation (EO) image classification (IC) in resource-constr…