Autonomous Spectral QA · Predictive Sensor Intelligence
SpectrIQ is a closed-loop QA and predictive intelligence platform for hyperspectral and multispectral sensors. 45 automated modules catch bad captures in real time — and a federated ML layer learns each sensor's degradation signature to forecast failure before tasking.
The Solution
Saturation, calibration drift, and illumination instability look identical to good data on any standard preview — until an analyst opens the cube days later. SpectrIQ closes that gap with patent-pending middleware that runs in real time, between the sensor and the pipeline.
Every scan runs through 45 quality checks — exposure, signal-to-noise, calibration drift, spectral shape, geometry, detector health — each rated Advisory, Degraded, Severe, or Fatal. Includes the patent-pending Shape Change Index (SCI) that catches VIS/NIR slope corruption invisible to mean-DN monitoring. SpectrIQ doesn't just flag failures: it adjusts integration time, triggers recapture, or halts acquisition before unrecoverable data is written.
Per-serial-number telemetry feeds a federated learning layer that models each sensor's degradation trajectory. Pre-mission readiness scores tell operators whether a camera should fly today — and what specifically is drifting. Fleet-wide pattern recognition compounds with scale: every deployed sensor makes every other sensor smarter.
Python pipeline with YAML-driven configuration, deployable on Jetson AGX Orin at the edge or on a ground station laptop. Sub-100ms per-frame latency. Sensor-agnostic by design — adding a new HSI or MSI camera is a config file, not a code change. Works in contested or disconnected environments.
Real-World Validation
We ran SpectrIQ across an operational MicaSense RedEdge vineyard dataset — 202 captures, real flights, real lighting conditions. The results show what's silently corrupting MSI data across the industry today.
Column gradients of 80–146% from oblique 30–45° camera angles. Systematic NDVI spatial bias invisible to per-band preview. Detected automatically, every frame.
SpectrIQ confirmed correct acquisitions just as reliably as it caught failures — high-confidence go/no-go decisions across the full dataset, not just outlier detection.
Missing bands from partial downloads identified before the operator left the field. Without SpectrIQ these would have shipped to processing as "complete" data.
SCI flagged VIS/NIR slope corruption that mean-DN and per-band monitors missed entirely — the exact failure mode that ruins multi-temporal vegetation analytics downstream.
Predictive Intelligence
Every sensor running SpectrIQ contributes anonymous QA telemetry — by serial number, environment, and failure mode — to a federated ML layer. Models learn each unit's specific degradation signature, then redistribute updated thresholds and failure forecasts back to every deployed sensor.
Operators get pre-mission readiness scores answering a question no one can answer today: is this camera ready to fly right now? Prediction accuracy compounds with fleet size. A competitor entering tomorrow cannot replicate this dataset — it only exists because of the network.
The Market
Every dollar of the global spectral imaging market depends on data that cannot currently be validated at acquisition time. SpectrIQ is the missing layer.
ISR in contested environments. Failed acquisitions are mission failures, not inconveniences.
185,000+ active drone cameras. A miscalibrated flight is a missed treatment window.
Structured QA output maps directly to 21 CFR Part 11 audit trails — compliance enabler, not cost.
Multi-temporal calibration consistency is the unsolved quality problem in commercial constellations.
Get Involved
SpectrIQ is patent pending and in active development. If you operate hyperspectral or multispectral sensors and want early access, or if you're an investor interested in the spectral imaging infrastructure space — we'd like to hear from you.
info@spectriq.io