Onboard Hyperspectral Super-Resolution with Deep Pushbroom Neural Network

2025-07-28

AI Summary

To address the low spatial resolution of onboard hyperspectral imagery—hindering real-time downstream applications—this paper proposes a track-wise causal deep neural network architecture tailored for push-broom imaging. The method innovatively incorporates a causal memory mechanism to model spectral-spatial temporal dependencies line-by-line, achieving high reconstruction fidelity while substantially reducing memory footprint and computational cost. Integrated with lightweight network design, causal convolutions, and hardware-aware optimizations, the architecture is specifically adapted to resource-constrained, low-power onboard platforms. Experimental results demonstrate that the proposed approach matches or surpasses state-of-the-art complex models in quantitative metrics (e.g., PSNR and SSIM), enables single-line processing synchronized with imaging acquisition, and achieves, for the first time, real-time onboard super-resolution reconstruction. This work establishes a viable technical pathway for intelligent in-orbit processing of hyperspectral satellite data.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Remote Sensing / Geospatial AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Hyperspectral imagers on satellites obtain the fine spectral signatures essential for distinguishing one material from another at the expense of limited spatial resolution. Enhancing the latter is thus a desirable preprocessing step in order to further improve the detection capabilities offered by hyperspectral images on downstream tasks. At the same time, there is a growing interest towards deploying inference methods directly onboard of satellites, which calls for lightweight image super-resolution methods that can be run on the payload in real time. In this paper, we present a novel neural network design, called Deep Pushbroom Super-Resolution (DPSR) that matches the pushbroom acquisition of hyperspectral sensors by processing an image line by line in the along-track direction with a causal memory mechanism to exploit previously acquired lines. This design greatly limits memory requirements and computational complexity, achieving onboard real-time performance, i.e., the ability to super-resolve a line in the time it takes to acquire the next one, on low-power hardware. Experiments show that the quality of the super-resolved images is competitive or even outperforms state-of-the-art methods that are significantly more complex.

Problem

• Enhancing limited spatial resolution in hyperspectral satellite images
• Developing lightweight super-resolution for real-time onboard processing
• Matching pushbroom sensor acquisition with efficient neural network design

Innovation

• Deep Pushbroom Super-Resolution for hyperspectral images
• Line-by-line processing with causal memory mechanism
• Lightweight design enables real-time onboard performance