Emissivity Correction Changes the Maintenance Decision: Reference-Free Validation of Material-Resolved Severity Grading in Proximal Thermal Remote Sensing of High-Voltage Substations

Proximal, ground-based infrared thermography of substation equipment grades a temperature rise computed under a single, camera-default emissivity and reflected-temperature setting applied to every pixel, regardless of the material it shows. This assumption is inexpensive but not harmless: two components at the same physical temperature but emissivities of 0.3 and 0.9 are read 24.0 K apart, enough to shift a component between adjacent bands of the ANSI/NETA MTS maintenance-priority scale. The measurement equation responsible sits on the same physical basis as land surface temperature retrieval from satellite and airborne thermal sensors, where emissivity is likewise the term most often assumed rather than measured. This paper corrects and validates that assumption at corpus scale by fusing the thermal frame with what a modern radiometric camera already records alongside it, a co-registered visible-light photograph, on a public 895-frame corpus of substation thermograms acquired under a single fixed set of acquisition parameters. The visible channel is used to rebuild the radiometric measurement equation, recover a per-frame reflected temperature from sky segmentation, and estimate in-situ emissivity at 106 metal-to-insulator material boundaries, with the resulting uncertainty propagated by a per-frame Monte Carlo to the severity band assigned to 765 graded frames. Three reference-free tests, a within-insulator negative control, a joint emissivity-reflectance fit, and a cross-view repeatability analysis, validate the estimated emissivity against physical constraints rather than a laboratory thermometer. Under the conventional absolute-temperature criterion, 48.4% of frames cannot be assigned a severity band with 80% confidence even once air temperature is obtained from meteorological reanalysis rather than a fixed default; grading instead against a photometrically similar in-frame reference surface reduces this to 5.9% (12.2% once the reference surface’s own sampling noise is additionally propagated through the same Monte Carlo). A value-of-information ranking identifies the single additional measurement, by material, that would most reduce this ambiguity. These results argue for relative grading criteria over a more accurate default emissivity table, an approach tied to the visible-thermal pairing common to modern radiometric cameras rather than to a specific camera model or acquisition site.