Spectral Fringe-adjusted Joint Transform Correlation based Efficient Object Classification in Hyperspectral Imagery

The spectral fringe-adjusted joint transform correlation (SFJTC) has been used effectively for performing deterministic target detection in hyperspectral imagery. However, experiments show decreased performance when noise-corrupted spectral variability is present in the target signatures. In this paper, we propose to use a modified spectral fringe-adjusted joint transform correlation based target detection algorithm, which employs a new real-valued filter called the logarithmic fringe-adjusted filter (LFAF). Furthermore, the maximum noise fraction (MNF) technique is used for preprocessing the hyperspectral imagery, which makes the SFJTC technique more insensitive to spectral variability in noisy environment. Test results using real life oil spill based hyperspectral image datasets show that the proposed scheme yields better performance compared to alternate techniques.