Hyperspectral anomaly detection based on spectral-spatial background joint sparse representation

In recent years, some algorithms based on sparse representation have been proposed to improve the detection performance for hyperspectral anomaly detection. Among these algorithms, the background joint sparse representation (BJSR) algorithm adaptively selects the most representative background bases for the local region and can obtain satisfactory results. However, BJSR mainly considers spectral characteristics of hyperspectral image. In this paper, we propose a BJSR-based spectral-spatial method. BJSR is first employed to process the original hyperspectral image in spectral domain. Then, linear local tangent space alignment (LLTSA) is used to obtain the low-dimensional manifold of the hyperspectral image. Next, spatial BJSR is used to process the low-dimensional manifold obtained by LLTSA. Finally, the proposed algorithm combines spectral BJSR with spatial BJSR to detect the anomaly targets. The experimental results demonstrate that the proposed algorithm can achieve a better performance when compared with the comparison algorithms.