In this paper, we consider change detection in the longwave infrared (LWIR) domain. Because thermal emission is the dominant radiation source in this domain, differences in temperature may appear as material changes and introduce false alarms in change imagery. Existing methods, such as temperature-emissivity separation and alpha residuals, attempt to extract temperature-independent LWIR spectral information. However, both methods remain susceptible to residual temperature effects which degrade change detection performance. Here, we develop temperature-robust versions of these algorithms that project the spectra into approximately temperatureinvariant subspaces. The complete error covariance matrix for each method is also derived so that Mahalanobis distance may be used to quantify spectral differences in the temperature-invariant domain. Examples using synthetic and measured data demonstrate substantial performance improvement relative to the baseline algorithms.
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