XingHuan Visible and Uncooled Multispectral Infrared Camera for Wildfire Detection: Algorithm Description and Initial Validation
Bibliographic record
Abstract
Advancements in wildfire detection using meteorological satellite data have progressed significantly since the 1980s, driven by improved instruments and analytical algorithms. In May 2024, the XingHuan satellite, designed for wildfire monitoring, was deployed into orbit. It carries a visible-panchromatic camera and a multispectral infrared imaging system, featuring a vanadium oxide array. The system provides a 60 m spatial resolution for visible-panchromatic imaging and 120 m for both mid-wave infrared and long-wave infrared imaging, all within a 1.5 kg payload. The camera’s high resolution, five spectral bands, and time-delay integration method significantly improve the signal-to-noise ratio (SNR) for detecting small wildfires. However, accurate radiometric quantification of the data depends on precise temperature measurement and thermal control system of the camera, which complicates fire pixel identification. This study proposes a novel approach, the fire dual-band local contrast method (Fire-DBLCM) for fire detection, using uncalibrated raw data from the XingHuan satellite. The Fire-DBLCM method selects an optimal background based on fire’s spectral properties, improving detection sensitivity. Inspired by the human visual system, this method circumvents the conventional fire detection process, such as radiometric calibration, atmospheric correction, cloud and water pixel classification, and absolute thresholding. Integrating both mid-wave and long-wave spectral bands further enhances the SNR compared to single band-dependent initial data and conventional local contrast method approaches. We evaluate the Fire-DBLCM’s performance by comparing its results with data from medium-resolution satellite sensors (MODIS and VIIRS) and high-resolution satellites (Landsat-8/9 and Sentinel-2). The findings indicate that the Fire-DBLCM algorithm identifies smaller wildfires more effectively than medium-resolution satellites and detects residual heat signatures more sensitively than high-resolution satellites. The smallest fires identified were$< $700 m2during the day and$< $100 m2at night, establishing a new benchmark for uncooled infrared satellite constellations with high spatiotemporal resolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".