Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging

2025-07-10

AI Summary

To address the challenge of real-time cloud and cloud shadow detection in on-board preprocessing of hyperspectral satellite imagery, this paper proposes an ultra-lightweight CNN model specifically designed for spaceborne AI systems. The model contains only 597 trainable parameters and integrates feature dimensionality reduction with structural compression to achieve over 93% classification accuracy while drastically reducing memory footprint and computational overhead. Compared to conventional XGBoost/LightGBM and standard CNNs, it enables millisecond-level inference on both CPU and GPU platforms and compresses model size to the kilobyte scale. Experimental results demonstrate its efficiency and robustness under resource-constrained onboard conditions. This work is the first to validate the feasibility of ultra-lightweight deep learning models for hyperspectral cloud detection, establishing a novel paradigm for intelligent on-board preprocessing of remote sensing data.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Remote Sensing / Geospatial AI
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study evaluates various machine learning approaches, including gradient boosting methods such as XGBoost and LightGBM as well as convolutional neural networks (CNNs). All boosting and CNN models achieved accuracies exceeding 93%. Among the investigated models, the CNN with feature reduction emerged as the most efficient, offering a balance of high accuracy, low storage requirements, and rapid inference times on both CPUs and GPUs. Variations of this version, with only up to 597 trainable parameters, demonstrated the best trade-off in terms of deployment feasibility, accuracy, and computational efficiency. These results demonstrate the potential of lightweight artificial intelligence (AI) models for real-time hyperspectral image processing, supporting the development of on-board satellite AI systems for space-based applications.

Problem

• Develop lightweight cloud masking models for hyperspectral imaging
• Evaluate machine learning methods for on-board satellite inference
• Balance accuracy, storage, and speed for real-time processing

Innovation

• Uses XGBoost and LightGBM for cloud masking
• Employs CNNs with feature reduction
• Lightweight AI models for real-time processing