Trends in oil spill detection via hyperspectral imaging

In hyperspectral imaging, pixels of interest generally incorporate information from disparate components which requires quantitative decomposition of these pixels to extract desired information. Since hyperspectral sensors collect data in hundreds of spectral bands, it is essential to perform spectral unmixing to identify the spectra of all endmembers in the pixel in order to ascertain the fractional abundances of pure target spectral signatures. By extracting desired spectral signature from high-dimensional remotely sensed hyperspectral imagery, one can detect and identify objects in vast geographical regions. While numerous algorithms were developed for target detection in hyperspectral imagery, a unified and synergistic approach to evaluate the performance of these algorithms for oil spill detection in ocean environment is yet to be done. Consequently, in this paper, we investigate and compare the performance of five most widely used target detection algorithms for the identification and tracking of surface and subsurface oil spills in ocean environment. Test results using real life oil spill based hyperspectral image datasets show that the spectral fringe-adjusted joint transform correlation technique and the constrained energy minimization technique yield better results compared to alternate techniques.