Anomaly Detection of Hyperspectral Image via Tensor Completion

In this letter, a novel method of anomaly detection for the hyperspectral image (HSI) is proposed. This method originates from two ideas. First, compared with the anomalies, the spectral curves of some (not all) backgrounds are usually easy to be accurately found. Second, the spectral curves of the missing pixels in the background can be recovered via the tensor completion technology. In this way, anomalies can be picked out via discriminating the background tensor and the original HSI. Specifically, some background pixels with low response in the detection map are first detected. Then, the selected background pixels with their spectral curves are utilized to reconstruct a three-order tensor whose elements are missing at some extent. Tensor completion technology is applied to this tensor and achieves a complete tensor, which depicts the background of the scene. Finally, the reconstructed tensor originated from the background pixels is discriminated from the original HSI to pick out the anomalies. The experimental data and performance analysis have demonstrated the effectiveness of the proposed method.