Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection

Nowadays, deep learning (DM) and tensor theory have become research hotspot in hyperspectral images (HSIs) processing. In this letter, transferred convolutional neural network (CNN) based on tensor (TCNNT) is proposed for hyperspectral anomaly detection (AD). TCNNT is an unsupervised DM framework and utilizes tensor structure to extract the spatial and spectral information of HSI effectively. First, the test tensor block centered at the test point is regarded as a tensor convolution kernel to convolve with the dictionary tensor blocks to extract deep feature, and the difference between the test tensor and the feature tensor is obtained. Then, the local neighboring tensor blocks are also regarded as tensor convolution kernels to convolve with the dictionary tensor blocks to extract deep feature, and the difference between the local neighboring tensor and the feature tensor is obtained. Finally, an adaptive model based on the above two differences is proposed for the detection output. Experiments conducted on one synthetic HSI and two real HSIs present superior performance of the proposed TCNNT.