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Comparison of bad pixel replacement techniques for LWIR hyperspectral imagery

This paper compares four different bad pixel replacement algorithms for LWIR hyperspectral imagery representing both physics-based unmixing and statistics-based methods. Testing is performed on a measured dataset using detection performance as a comparison metric and synthetic dataset with reconstruction error and detection performance used to compare. It is found that a statistics-based covariance matched filter method generally performs best of the four methods tested but at significantly greater computational cost. A simple plate metaphor interpolation is the fastest technique but struggles to correct pixels where sharp spectral or spatial difference are present. Meanwhile, an endmember-based unmixing approach provided a balance between the two in terms of computational complextity and reconstruction performance.