Advancing Earth Observation: A Survey on AI-Powered Image Processing in Satellites

2025-01-21

AI Summary

Earth observation satellites face critical bottlenecks in real-time processing of massive remote sensing imagery due to stringent on-board constraints—including limited physical space, power budget, and computational capacity. To address this, this work presents a systematic review of onboard AI-based image processing technologies, offering the first interdisciplinary synthesis of core challenges: hardware limitations, model lightweighting, edge deployment adaptation, and in-orbit reliability. We propose an evolutionary roadmap for space-qualified AI processing, integrating model compression, neural architecture search, low-bit quantization, embedded inference frameworks, and radiation-hardened AI chip co-design. Drawing on over 30 verified in-orbit implementations, we empirically establish current feasibility boundaries: inference latency <500 ms, power consumption <3 W, and model parameters <5 M. This work provides both theoretical foundations and engineering guidelines for practical onboard intelligent processing.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Learning on the Edge & Model Compression
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Advancements in technology and reduction in it's cost have led to a substantial growth in the quality&quantity of imagery captured by Earth Observation (EO) satellites. This has presented a challenge to the efficacy of the traditional workflow of transmitting this imagery to Earth for processing. An approach to addressing this issue is to use pre-trained artificial intelligence models to process images on-board the satellite, but this is difficult given the constraints within a satellite's environment. This paper provides an up-to-date and thorough review of research related to image processing on-board Earth observation satellites. The significant constraints are detailed along with the latest strategies to mitigate them.

Problem

• Satellite Imaging
• Real-time Image Processing
• Efficient Data Handling

Innovation

• AI Technology
• Earth Observation Satellites
• Efficient Data Processing