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

arXiv: https://arxiv.org/abs/2507.08052 · HTML: https://mlsanity.com/paper/2507.08052/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward](https://mlsanity.com/paper/2305.08413/) (2023)
- [The SVD of Convolutional Weights: A CNN Interpretability Framework](https://mlsanity.com/paper/2208.06894/) (2022)
- [Applications of Knowledge Distillation in Remote Sensing: A Survey](https://mlsanity.com/paper/2409.12111/) (2024)
- [An Open Hyperspectral Dataset with Sea-Land-Cloud Ground-Truth from the HYPSO-1 Satellite](https://mlsanity.com/paper/2308.13679/) (2023)
- [Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning](https://mlsanity.com/paper/2310.16210/) (2023)
- [Efficient FPGA-accelerated Convolutional Neural Networks for Cloud Detection on CubeSats](https://mlsanity.com/paper/2504.03891/) (2025)
- [An Energy-Efficient Artefact Detection Accelerator on FPGAs for Hyper-Spectral Satellite Imagery](https://mlsanity.com/paper/2407.17647/) (2024)
- [TTML: tensor trains for general supervised machine learning](https://mlsanity.com/paper/2203.04352/) (2022)
- [FPGA-Enabled Machine Learning Applications in Earth Observation: A Systematic Review](https://mlsanity.com/paper/2506.03938/) (2025)

## Abstract (arXiv)

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.