PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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 andGenesis 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 (8 May 2018); https://doi.org/10.1117/12.2303508
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Jacob A. Martin, 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 (8 May 2018); https://doi.org/10.1117/12.2303508