Toward a Unified Semantic Loss Model for Deep JSCC-based Transmission of EO Imagery

2026-01-28

AI Summary

This work addresses the challenge of transmitting high-resolution Earth observation (EO) imagery under stringent satellite communication constraints—including limited bandwidth, power, and dynamic channel conditions—where conventional transmission methods struggle to balance reconstruction fidelity and downstream task performance. The authors propose a unified semantic loss framework that jointly models reconstruction quality and task accuracy, enabling a deep joint source-channel coding (DJSCC) system that integrates both reconstruction- and task-oriented objectives. The approach elucidates the intrinsic relationship among compression ratio, channel signal-to-noise ratio, and semantic quality, and leverages lightweight task-specific models such as EfficientViT for efficient end-to-end optimization. Experimental results demonstrate that the proposed framework significantly enhances semantic transmission efficiency for EO images under low-bandwidth and adverse channel conditions, achieving a superior trade-off between image reconstruction quality and accuracy in downstream tasks such as classification and detection.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Modern Earth Observation (EO) systems increasingly rely on high-resolution imagery to support critical applications such as environmental monitoring, disaster response, and land-use analysis. Although these applications benefit from detailed visual data, the resulting data volumes impose significant challenges on satellite communication systems constrained by limited bandwidth, power, and dynamic link conditions. To address these limitations, this paper investigates Deep Joint Source-Channel Coding (DJSCC) as an effective source-channel paradigm for the transmission of EO imagery. We focus on two complementary aspects of semantic loss in DJSCC-based systems. First, a reconstruction-centric framework is evaluated by analyzing the semantic degradation of reconstructed images under varying compression ratios and channel signal-to-noise ratios (SNR). Second, a task-oriented framework is developed by integrating DJSCC with lightweight, application-specific models (e.g., EfficientViT), with performance measured using downstream task accuracy rather than pixel-level fidelity. Based on extensive empirical analysis, we propose a unified semantic loss framework that captures both reconstruction-centric and task-oriented performance within a single model. This framework characterizes the implicit relationship between JSCC compression, channel SNR, and semantic quality, offering actionable insights for the design of robust and efficient EO imagery transmission under resource-constrained satellite links.

Problem

• Earth Observation
• Semantic Loss
• Deep Joint Source-Channel Coding
• Satellite Communication
• Image Transmission

Innovation

• Deep Joint Source-Channel Coding
• Semantic Loss
• Task-Oriented Transmission
• Earth Observation Imagery
• Unified Framework