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.
Jacob A. Martin and Genesis Islas, "Comparison of bad pixel replacement techniques for LWIR hyperspectral imagery," Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440V (Presented at SPIE Defense + Security: April 18, 2018; Published: 8 May 2018); https://doi.org/10.1117/12.2303508.
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