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Hyperspectral images are constantly improving their spectral resolution and spatial resolution，so utilizing hyperspectral images for object detection has become a research hotspot in the field of hyperspectral remote sensing. Anomaly detection is the main way to achieve object detection. Abnormal targets in hyperspectral images are usually composed of a few pixels (or even sub-pixel) that are clearly different from the surrounding background pixels. Compared with the background, abnormal targets have two characteristics: spectral anomaly and spatial anomaly. Traditional hyperspectral image anomaly detection methods only utilize spectral anomalies and ignore spatial anomalies between pixels. Hyperspectral images can be represented by third-order tensors, where the first two orders of the third-order tensor are used to represent the spatial dimension of the image (i.e. the height and width of the image), and the third order is used to represent the spectral dimension. Therefore, tensor decomposition can simultaneously represent the spatial and spectral features of anomalous targets. This paper proposes a new anomaly detection method based on tensor decomposition and information entropy. This method is mainly divided into three steps. Firstly, a third-order tensor is used to represent the cube of the detected hyperspectral image, and the Tucker decomposition of the third-order tensor is applied to the detected hyperspectral image. Secondly, the background information in the detected hyperspectral image is removed using information entropy, and the remaining feature components are reconstructed into the hyperspectral image. Thirdly, the RX algorithm is used to detect anomalies in the reconstructed hyperspectral image. Compared with methods based on spectral anomalies, this method has better detection efficiency.
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