Dictionary Learning Hyperspectral Target Detection Algorithm Based on Tucker Tensor Decomposition

1As a research hotspot, hyperspectral image target detection is more and more widely used in military and civilian fields. In order to make use of the spatial and spectrum information of hyperspectral image data at the same time, a new dictionary learning hyperspectral image target detection algorithm based on Tucker tensor decomposition is proposed in this paper. The algorithm uses Tucker tensor decomposition to extract effective local image block spatial spectrum features. A detection model based on sparse representation and collaborative representation is established, and experiments are carried out on two representative hyperspectral images data. From the visual detection results, the algorithm effectively extracts the spatial spectrum features in the complex background and strong noise environment, has a good ability to suppress the background, and the detection target is significant.