Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware

• URL: http://arxiv.org/abs/2507.01472v1
• Date: Wed, 02 Jul 2025 08:34:34 GMT
• Title: Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware
• Authors: Jonáš Herec, Vít Růžička, Rado Pitoňák,
• Abstract summary: Methane is a potent greenhouse gas, and detecting its leaks early via hyperspectral satellite imagery can help mitigate climate change.<n>Traditional methane enhancement methods are too computationally demanding for resource-limited onboard hardware.<n>This work accelerates methane detection by focusing on efficient, low-power algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Methane is a potent greenhouse gas, and detecting its leaks early via hyperspectral satellite imagery can help mitigate climate change. Meanwhile, many existing missions operate in manual tasking regimes only, thus missing potential events of interest. To overcome slow downlink rates cost-effectively, onboard detection is a viable solution. However, traditional methane enhancement methods are too computationally demanding for resource-limited onboard hardware. This work accelerates methane detection by focusing on efficient, low-power algorithms. We test fast target detection methods (ACE, CEM) that have not been previously used for methane detection and propose a Mag1c-SAS - a significantly faster variant of the current state-of-the-art algorithm for methane detection: Mag1c. To explore their true detection potential, we integrate them with a machine learning model (U-Net, LinkNet). Our results identify two promising candidates (Mag1c-SAS and CEM), both acceptably accurate for the detection of strong plumes and computationally efficient enough for onboard deployment: one optimized more for accuracy, the other more for speed, achieving up to ~100x and ~230x faster computation than original Mag1c on resource-limited hardware. Additionally, we propose and evaluate three band selection strategies. One of them can outperform the method traditionally used in the field while using fewer channels, leading to even faster processing without compromising accuracy. This research lays the foundation for future advancements in onboard methane detection with minimal hardware requirements, improving timely data delivery. The produced code, data, and models are open-sourced and can be accessed from https://github.com/zaitra/methane-filters-benchmark.

Related papers

• Energy-Efficient Fast Object Detection on Edge Devices for IoT Systems [0.0]
  This paper presents an Internet of Things (IoT) application that utilizes an AI classifier for fast-object detection using the frame difference method.<n>We have implemented this technique on three edge devices: AMD AlveoT M U50, Jetson Orin Nano, and Hailo-8T M AI Accelerator.<n>We examined various classes, including birds, cars, trains, and airplanes.<n>The proposed algorithm has improved the average accuracy gain by 28.314%, the average efficiency gain by 3.6 times, and the average latency reduction by 39.305% compared to the end-to-end method.
  arXiv  Detail & Related papers  (2026-02-10T08:18:06Z)
• AttMetNet: Attention-Enhanced Deep Neural Network for Methane Plume Detection in Sentinel-2 Satellite Imagery [1.0266286487433587]
  Methane is a powerful greenhouse gas that contributes significantly to global warming.<n>We present AttMetNet, a novel attention-enhanced deep learning framework for methane plume detection with Sentinel-2 satellite imagery.
  arXiv  Detail & Related papers  (2025-12-02T13:34:39Z)
• Towards Methane Detection Onboard Satellites [0.5900825203015314]
  Methane is a potent greenhouse gas and a major driver of climate change.<n>Machine learning (ML) deployed onboard satellites can enable rapid detection while reducing downlink costs.<n>We introduce a novel approach that bypasses these preprocessing steps by using textitunorthorectified data (UnorthoDOS)
  arXiv  Detail & Related papers  (2025-08-30T22:54:00Z)
• Unfolding Target Detection with State Space Model [8.493729039825332]
  We introduce a novel method that combines signal processing and deep learning by unfolding the CFAR detector with a state space model architecture. By reserving the CFAR pipeline yet turning its sophisticated configurations into trainable parameters, our method achieves high detection performance without manual parameter tuning. The results highlight the remarkable performance of the proposed method, outperforming CFAR and its variants by 10X in detection rate and false alarm rate.
  arXiv  Detail & Related papers  (2024-10-30T07:43:18Z)
• Machine Learning for Methane Detection and Quantification from Space -- A survey [49.7996292123687]
  Methane (CH_4) is a potent anthropogenic greenhouse gas, contributing 86 times more to global warming than Carbon Dioxide (CO_2) over 20 years. This work expands existing information on operational methane point source detection sensors in the Short-Wave Infrared (SWIR) bands. It reviews the state-of-the-art for traditional as well as Machine Learning (ML) approaches.
  arXiv  Detail & Related papers  (2024-08-27T15:03:20Z)
• Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA [20.629635991749808]
  This paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs. At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads. At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient BayesNNs.
  arXiv  Detail & Related papers  (2024-06-20T17:08:42Z)
• Graspness Discovery in Clutters for Fast and Accurate Grasp Detection [57.81325062171676]
  "graspness" is a quality based on geometry cues that distinguishes graspable areas in cluttered scenes. We develop a neural network named cascaded graspness model to approximate the searching process. Experiments on a large-scale benchmark, GraspNet-1Billion, show that our method outperforms previous arts by a large margin.
  arXiv  Detail & Related papers  (2024-06-17T02:06:47Z)
• Efficient One-stage Video Object Detection by Exploiting Temporal Consistency [35.16197118579414]
  One-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. In this paper, we first analyse the computational bottlenecks of using one-stage detectors for video object detection. We present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD.
  arXiv  Detail & Related papers  (2024-02-14T15:32:07Z)
• Optimizing ship detection efficiency in SAR images [12.829941550630776]
  The speed and compute cost of vessel detection are essential for a timely intervention to prevent illegal fishing. We trained an object detection model based on a convolutional neural network (CNN) using a dataset of satellite images. We show that by using a classification model the average precision of the detection model can be approximated to 99.5% in 44% of the time or to 92.7% in 25% of the time.
  arXiv  Detail & Related papers  (2022-12-12T12:04:10Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge [110.7678032481059]
  We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings.
  arXiv  Detail & Related papers  (2022-09-22T16:44:59Z)
• ETAD: A Unified Framework for Efficient Temporal Action Detection [70.21104995731085]
  Untrimmed video understanding such as temporal action detection (TAD) often suffers from the pain of huge demand for computing resources. We build a unified framework for efficient end-to-end temporal action detection (ETAD) ETAD achieves state-of-the-art performance on both THUMOS-14 and ActivityNet-1.3.
  arXiv  Detail & Related papers  (2022-05-14T21:16:21Z)
• Efficient Few-Shot Object Detection via Knowledge Inheritance [62.36414544915032]
  Few-shot object detection (FSOD) aims at learning a generic detector that can adapt to unseen tasks with scarce training samples. We present an efficient pretrain-transfer framework (PTF) baseline with no computational increment. We also propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights.
  arXiv  Detail & Related papers  (2022-03-23T06:24:31Z)
• Sample and Computation Redistribution for Efficient Face Detection [137.19388513633484]
  Training data sampling and computation distribution strategies are the keys to efficient and accurate face detection. scrfdf34 outperforms the best competitor, TinaFace, by $3.86%$ (AP at hard set) while being more than emph3$times$ faster on GPUs with VGA-resolution images.
  arXiv  Detail & Related papers  (2021-05-10T23:51:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.