ELMZip: Onboard Satellite Image Compression via Extreme Learning Machines for Efficient Downlink

2026-08-07

AI Summary

This study addresses the downlink bottleneck in small satellite multispectral imaging, where large data volumes and limited communication windows challenge conventional compression methods that struggle with the nonlinear statistical characteristics of multi-band, multi-resolution imagery. To overcome this, the paper proposes ELMZip, a novel on-board image compression framework that introduces extreme learning machines (ELMs) into spaceborne processing. By integrating domain decomposition and random feature mapping, ELMZip formulates image representation as a convex least-squares problem, enabling efficient neural implicit modeling without backpropagation. An asymmetric protocol transmits only compact output weights, drastically reducing downlink payload. The approach achieves high-fidelity reconstruction while substantially minimizing data return volume, thereby enabling real-time remote sensing analytics on resource-constrained platforms.

Abstract

The acquisition of multispectral imagery via small satellites (e.g., CubeSats) presents significant data downlink challenges due to high data volumes and restricted communication windows. While onboard image compression is critical to address this bottleneck, traditional methods often struggle to adapt to the nonlinear statistics of multi-band, multi-resolution data. To overcome these limitations, we propose ELMZip, a novel framework based on Extreme Learning Machines (ELM) and domain decomposition strategies for efficient, resolution-free onboard neural representation. ELMZip formulates the fitting process as a convex least-squares problem using random-feature single-layer networks, thereby eliminating the need for computationally expensive backpropagation. By adopting an asymmetric transmission protocol that sends only the compact output weights, the proposed method significantly reduces the downlink payload. Unlike previous neural representation approaches that rely on iterative optimization and require transmitting full network parameters, ELMZip achieves significant compression efficiency while maintaining high reconstruction fidelity. This capability enables immediate image reconstruction for analysis, allowing resource-constrained platforms to maximize data return and advancing real-time AI-powered Earth observation.

Problem

• satellite image compression
• downlink bottleneck
• multispectral imagery
• onboard processing
• nonlinear statistics

Innovation

• Extreme Learning Machine
• Onboard Compression
• Neural Representation
• Asymmetric Transmission
• Satellite Imagery