Automatic spectral signature extraction for hyperspectral target detection

There are two main approaches to hyperspectral target detection: anomaly detection techniques, which detect outliers substantially different from the background, and spectral signature techniques, which require as an input a user-defined target signature. Oftentimes, however, the target signature may not be known, or there may be unexpected targets in the image, which are unknown but still of interest. As a result, algorithms that can automatically extract potential target signatures without any a priori knowledge are of great interest. In this work, a fusion-based algorithm is developed that takes advantage of both spatial and spectral information to automatically extract the spectral signatures of potential targets of interest. The performance of several target detection algorithms is compared for both the proposed spatially-spectrally estimated (SSE) target signature and the initial target signature used by the Automatic Target Detection and Classification Algorithm (ATDCA). It is shown that the SSE signature leads to improved automatic spectral target recognition (ATSR) performance than the ATDCA algorithm for the test conducted on the AVIRIS Indian Pines dataset.